From cb4fb6ca3e17152a3e14c28821d488ae4dc87f30 Mon Sep 17 00:00:00 2001
From: luisgruber <71277638+luisgruber@users.noreply.github.com>
Date: Thu, 29 Aug 2024 19:42:36 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20luisgrub?=
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---
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diff --git a/404.html b/404.html
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diff --git a/LICENSE.html b/LICENSE.html
index 882042a..688f3a9 100644
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diff --git a/articles/bayesianVARs-vignette.pdf b/articles/bayesianVARs-vignette.pdf
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@@ -49,9 +34,9 @@
       <img src="logo.png" class="logo" alt=""><h1>Authors and Citation</h1>
     </div>
 
-    <div class="section level2 citation">
+    <div class="section level2">
       <h2>Authors</h2>
-      
+
       <ul class="list-unstyled"><li>
           <p><strong>Luis Gruber</strong>. Copyright holder, author, maintainer. <a href="https://orcid.org/0000-0002-2399-738X" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
           </p>
@@ -62,25 +47,26 @@ <h2>Authors</h2>
         </li>
       </ul></div>
 
-    <div class="section level2 citation">
+    <div class="section level2">
       <h2 id="citation">Citation</h2>
-      <p><small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
+      <p><small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
       <p>Gruber L (2024).
 <em>bayesianVARs: MCMC Estimation of Bayesian Vectorautoregressions</em>.
-R package version 0.1.3, 
-https://luisgruber.github.io/bayesianVARs/, <a href="https://github.com/luisgruber/bayesianVARs" class="external-link">https://github.com/luisgruber/bayesianVARs</a>. 
+R package version 0.1.3.9000,
+https://luisgruber.github.io/bayesianVARs/, <a href="https://github.com/luisgruber/bayesianVARs" class="external-link">https://github.com/luisgruber/bayesianVARs</a>.
 </p>
       <pre>@Manual{,
   title = {bayesianVARs: MCMC Estimation of Bayesian Vectorautoregressions},
   author = {Luis Gruber},
   year = {2024},
-  note = {R package version 0.1.3, 
-https://luisgruber.github.io/bayesianVARs/},
+  note = {R package version 0.1.3.9000,
+    https://luisgruber.github.io/bayesianVARs/},
   url = {https://github.com/luisgruber/bayesianVARs},
 }</pre>
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@@ -2,4 +2,4 @@
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-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) 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var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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.show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: .5rem;--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: .5rem;--bs-navbar-toggler-padding-y: .25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2833,37,41,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23052c65'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #86b7fe;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(13,110,253,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: 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var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 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-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) 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var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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.show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: .5rem;--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: .5rem;--bs-navbar-toggler-padding-y: .25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2833,37,41,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23052c65'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #86b7fe;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(13,110,253,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: 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var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 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diff --git a/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js b/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
new file mode 100644
index 0000000..c628326
--- /dev/null
+++ b/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
@@ -0,0 +1,5 @@
+/*!
+ * Bootstrap Table of Contents v1.0.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
+!function(a){"use strict";window.Toc={helpers:{findOrFilter:function(e,t){var n=e.find(t);return e.filter(t).add(n).filter(":not([data-toc-skip])")},generateUniqueIdBase:function(e){return a(e).text().trim().replace(/\'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]\.\/\(\)\*\\\n\t\b\v]/g,"-").replace(/-{2,}/g,"-").substring(0,64).replace(/^-+|-+$/gm,"").toLowerCase()||e.tagName.toLowerCase()},generateUniqueId:function(e){for(var t=this.generateUniqueIdBase(e),n=0;;n++){var r=t;if(0<n&&(r+="-"+n),!document.getElementById(r))return r}},generateAnchor:function(e){if(e.id)return e.id;var t=this.generateUniqueId(e);return e.id=t},createNavList:function(){return a('<ul class="nav navbar-nav"></ul>')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('<a class="nav-link"></a>');n.attr("href","#"+e),n.text(t);var r=a("<li></li>");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1<this.findOrFilter(e,"h"+t).length)return t}return 1},getHeadings:function(e,t){var n="h"+t,r="h"+(t+1);return this.findOrFilter(e,n+","+r)},getNavLevel:function(e){return parseInt(e.tagName.charAt(1),10)},populateNav:function(r,a,e){var i,s=r,c=this;e.each(function(e,t){var n=c.generateNavItem(t);c.getNavLevel(t)===a?s=r:i&&s===r&&(s=c.createChildNavList(i)),s.append(n),i=n})},parseOps:function(e){var t;return(t=e.jquery?{$nav:e}:e).$scope=t.$scope||a(document.body),t}},init:function(e){(e=this.helpers.parseOps(e)).$nav.attr("data-toggle","toc");var t=this.helpers.createChildNavList(e.$nav),n=this.helpers.getTopLevel(e.$scope),r=this.helpers.getHeadings(e.$scope,n);this.helpers.populateNav(t,n,r)}},a(function(){a('nav[data-toggle="toc"]').each(function(e,t){var n=a(t);Toc.init(n)})})}(jQuery);
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diff --git a/deps/clipboard.js-2.0.11/clipboard.min.js b/deps/clipboard.js-2.0.11/clipboard.min.js
new file mode 100644
index 0000000..1103f81
--- /dev/null
+++ b/deps/clipboard.js-2.0.11/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},n="";return"string"==typeof t?n=o(t,e):t instanceof HTMLInputElement&&!["text","search","url","tel","password"].includes(null==t?void 0:t.type)?n=o(t.value,e):(n=r()(t),c("copy")),n};function l(t){return(l="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==l(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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diff --git a/deps/data-deps.txt b/deps/data-deps.txt
index 693b82d..8aa49e2 100644
--- a/deps/data-deps.txt
+++ b/deps/data-deps.txt
@@ -2,3 +2,12 @@
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet" />
 <script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script>
+<link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet" />
+<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet" />
+<script src="deps/headroom-0.11.0/headroom.min.js"></script>
+<script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script>
+<script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script>
+<script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script>
+<script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script>
+<script src="deps/search-1.0.0/fuse.min.js"></script>
+<script src="deps/search-1.0.0/mark.min.js"></script>
diff --git a/deps/font-awesome-6.4.2/css/all.css b/deps/font-awesome-6.4.2/css/all.css
new file mode 100644
index 0000000..bdb6e3a
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/all.css
@@ -0,0 +1,7968 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa {
+  font-family: var(--fa-style-family, "Font Awesome 6 Free");
+  font-weight: var(--fa-style, 900); }
+
+.fa,
+.fa-classic,
+.fa-sharp,
+.fas,
+.fa-solid,
+.far,
+.fa-regular,
+.fab,
+.fa-brands {
+  -moz-osx-font-smoothing: grayscale;
+  -webkit-font-smoothing: antialiased;
+  display: var(--fa-display, inline-block);
+  font-style: normal;
+  font-variant: normal;
+  line-height: 1;
+  text-rendering: auto; }
+
+.fas,
+.fa-classic,
+.fa-solid,
+.far,
+.fa-regular {
+  font-family: 'Font Awesome 6 Free'; }
+
+.fab,
+.fa-brands {
+  font-family: 'Font Awesome 6 Brands'; }
+
+.fa-1x {
+  font-size: 1em; }
+
+.fa-2x {
+  font-size: 2em; }
+
+.fa-3x {
+  font-size: 3em; }
+
+.fa-4x {
+  font-size: 4em; }
+
+.fa-5x {
+  font-size: 5em; }
+
+.fa-6x {
+  font-size: 6em; }
+
+.fa-7x {
+  font-size: 7em; }
+
+.fa-8x {
+  font-size: 8em; }
+
+.fa-9x {
+  font-size: 9em; }
+
+.fa-10x {
+  font-size: 10em; }
+
+.fa-2xs {
+  font-size: 0.625em;
+  line-height: 0.1em;
+  vertical-align: 0.225em; }
+
+.fa-xs {
+  font-size: 0.75em;
+  line-height: 0.08333em;
+  vertical-align: 0.125em; }
+
+.fa-sm {
+  font-size: 0.875em;
+  line-height: 0.07143em;
+  vertical-align: 0.05357em; }
+
+.fa-lg {
+  font-size: 1.25em;
+  line-height: 0.05em;
+  vertical-align: -0.075em; }
+
+.fa-xl {
+  font-size: 1.5em;
+  line-height: 0.04167em;
+  vertical-align: -0.125em; }
+
+.fa-2xl {
+  font-size: 2em;
+  line-height: 0.03125em;
+  vertical-align: -0.1875em; }
+
+.fa-fw {
+  text-align: center;
+  width: 1.25em; }
+
+.fa-ul {
+  list-style-type: none;
+  margin-left: var(--fa-li-margin, 2.5em);
+  padding-left: 0; }
+  .fa-ul > li {
+    position: relative; }
+
+.fa-li {
+  left: calc(var(--fa-li-width, 2em) * -1);
+  position: absolute;
+  text-align: center;
+  width: var(--fa-li-width, 2em);
+  line-height: inherit; }
+
+.fa-border {
+  border-color: var(--fa-border-color, #eee);
+  border-radius: var(--fa-border-radius, 0.1em);
+  border-style: var(--fa-border-style, solid);
+  border-width: var(--fa-border-width, 0.08em);
+  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); }
+
+.fa-pull-left {
+  float: left;
+  margin-right: var(--fa-pull-margin, 0.3em); }
+
+.fa-pull-right {
+  float: right;
+  margin-left: var(--fa-pull-margin, 0.3em); }
+
+.fa-beat {
+  -webkit-animation-name: fa-beat;
+          animation-name: fa-beat;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-bounce {
+  -webkit-animation-name: fa-bounce;
+          animation-name: fa-bounce;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); }
+
+.fa-fade {
+  -webkit-animation-name: fa-fade;
+          animation-name: fa-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-beat-fade {
+  -webkit-animation-name: fa-beat-fade;
+          animation-name: fa-beat-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-flip {
+  -webkit-animation-name: fa-flip;
+          animation-name: fa-flip;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-shake {
+  -webkit-animation-name: fa-shake;
+          animation-name: fa-shake;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 2s);
+          animation-duration: var(--fa-animation-duration, 2s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin-reverse {
+  --fa-animation-direction: reverse; }
+
+.fa-pulse,
+.fa-spin-pulse {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, steps(8));
+          animation-timing-function: var(--fa-animation-timing, steps(8)); }
+
+@media (prefers-reduced-motion: reduce) {
+  .fa-beat,
+  .fa-bounce,
+  .fa-fade,
+  .fa-beat-fade,
+  .fa-flip,
+  .fa-pulse,
+  .fa-shake,
+  .fa-spin,
+  .fa-spin-pulse {
+    -webkit-animation-delay: -1ms;
+            animation-delay: -1ms;
+    -webkit-animation-duration: 1ms;
+            animation-duration: 1ms;
+    -webkit-animation-iteration-count: 1;
+            animation-iteration-count: 1;
+    -webkit-transition-delay: 0s;
+            transition-delay: 0s;
+    -webkit-transition-duration: 0s;
+            transition-duration: 0s; } }
+
+@-webkit-keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@-webkit-keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@-webkit-keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@-webkit-keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@-webkit-keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@-webkit-keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+.fa-rotate-90 {
+  -webkit-transform: rotate(90deg);
+          transform: rotate(90deg); }
+
+.fa-rotate-180 {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg); }
+
+.fa-rotate-270 {
+  -webkit-transform: rotate(270deg);
+          transform: rotate(270deg); }
+
+.fa-flip-horizontal {
+  -webkit-transform: scale(-1, 1);
+          transform: scale(-1, 1); }
+
+.fa-flip-vertical {
+  -webkit-transform: scale(1, -1);
+          transform: scale(1, -1); }
+
+.fa-flip-both,
+.fa-flip-horizontal.fa-flip-vertical {
+  -webkit-transform: scale(-1, -1);
+          transform: scale(-1, -1); }
+
+.fa-rotate-by {
+  -webkit-transform: rotate(var(--fa-rotate-angle, none));
+          transform: rotate(var(--fa-rotate-angle, none)); }
+
+.fa-stack {
+  display: inline-block;
+  height: 2em;
+  line-height: 2em;
+  position: relative;
+  vertical-align: middle;
+  width: 2.5em; }
+
+.fa-stack-1x,
+.fa-stack-2x {
+  left: 0;
+  position: absolute;
+  text-align: center;
+  width: 100%;
+  z-index: var(--fa-stack-z-index, auto); }
+
+.fa-stack-1x {
+  line-height: inherit; }
+
+.fa-stack-2x {
+  font-size: 2em; }
+
+.fa-inverse {
+  color: var(--fa-inverse, #fff); }
+
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+readers do not read off random characters that represent icons */
+
+.fa-0::before {
+  content: "\30"; }
+
+.fa-1::before {
+  content: "\31"; }
+
+.fa-2::before {
+  content: "\32"; }
+
+.fa-3::before {
+  content: "\33"; }
+
+.fa-4::before {
+  content: "\34"; }
+
+.fa-5::before {
+  content: "\35"; }
+
+.fa-6::before {
+  content: "\36"; }
+
+.fa-7::before {
+  content: "\37"; }
+
+.fa-8::before {
+  content: "\38"; }
+
+.fa-9::before {
+  content: "\39"; }
+
+.fa-fill-drip::before {
+  content: "\f576"; }
+
+.fa-arrows-to-circle::before {
+  content: "\e4bd"; }
+
+.fa-circle-chevron-right::before {
+  content: "\f138"; }
+
+.fa-chevron-circle-right::before {
+  content: "\f138"; }
+
+.fa-at::before {
+  content: "\40"; }
+
+.fa-trash-can::before {
+  content: "\f2ed"; }
+
+.fa-trash-alt::before {
+  content: "\f2ed"; }
+
+.fa-text-height::before {
+  content: "\f034"; }
+
+.fa-user-xmark::before {
+  content: "\f235"; }
+
+.fa-user-times::before {
+  content: "\f235"; }
+
+.fa-stethoscope::before {
+  content: "\f0f1"; }
+
+.fa-message::before {
+  content: "\f27a"; }
+
+.fa-comment-alt::before {
+  content: "\f27a"; }
+
+.fa-info::before {
+  content: "\f129"; }
+
+.fa-down-left-and-up-right-to-center::before {
+  content: "\f422"; }
+
+.fa-compress-alt::before {
+  content: "\f422"; }
+
+.fa-explosion::before {
+  content: "\e4e9"; }
+
+.fa-file-lines::before {
+  content: "\f15c"; }
+
+.fa-file-alt::before {
+  content: "\f15c"; }
+
+.fa-file-text::before {
+  content: "\f15c"; }
+
+.fa-wave-square::before {
+  content: "\f83e"; }
+
+.fa-ring::before {
+  content: "\f70b"; }
+
+.fa-building-un::before {
+  content: "\e4d9"; }
+
+.fa-dice-three::before {
+  content: "\f527"; }
+
+.fa-calendar-days::before {
+  content: "\f073"; }
+
+.fa-calendar-alt::before {
+  content: "\f073"; }
+
+.fa-anchor-circle-check::before {
+  content: "\e4aa"; }
+
+.fa-building-circle-arrow-right::before {
+  content: "\e4d1"; }
+
+.fa-volleyball::before {
+  content: "\f45f"; }
+
+.fa-volleyball-ball::before {
+  content: "\f45f"; }
+
+.fa-arrows-up-to-line::before {
+  content: "\e4c2"; }
+
+.fa-sort-down::before {
+  content: "\f0dd"; }
+
+.fa-sort-desc::before {
+  content: "\f0dd"; }
+
+.fa-circle-minus::before {
+  content: "\f056"; }
+
+.fa-minus-circle::before {
+  content: "\f056"; }
+
+.fa-door-open::before {
+  content: "\f52b"; }
+
+.fa-right-from-bracket::before {
+  content: "\f2f5"; }
+
+.fa-sign-out-alt::before {
+  content: "\f2f5"; }
+
+.fa-atom::before {
+  content: "\f5d2"; }
+
+.fa-soap::before {
+  content: "\e06e"; }
+
+.fa-icons::before {
+  content: "\f86d"; }
+
+.fa-heart-music-camera-bolt::before {
+  content: "\f86d"; }
+
+.fa-microphone-lines-slash::before {
+  content: "\f539"; }
+
+.fa-microphone-alt-slash::before {
+  content: "\f539"; }
+
+.fa-bridge-circle-check::before {
+  content: "\e4c9"; }
+
+.fa-pump-medical::before {
+  content: "\e06a"; }
+
+.fa-fingerprint::before {
+  content: "\f577"; }
+
+.fa-hand-point-right::before {
+  content: "\f0a4"; }
+
+.fa-magnifying-glass-location::before {
+  content: "\f689"; }
+
+.fa-search-location::before {
+  content: "\f689"; }
+
+.fa-forward-step::before {
+  content: "\f051"; }
+
+.fa-step-forward::before {
+  content: "\f051"; }
+
+.fa-face-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-flag-checkered::before {
+  content: "\f11e"; }
+
+.fa-football::before {
+  content: "\f44e"; }
+
+.fa-football-ball::before {
+  content: "\f44e"; }
+
+.fa-school-circle-exclamation::before {
+  content: "\e56c"; }
+
+.fa-crop::before {
+  content: "\f125"; }
+
+.fa-angles-down::before {
+  content: "\f103"; }
+
+.fa-angle-double-down::before {
+  content: "\f103"; }
+
+.fa-users-rectangle::before {
+  content: "\e594"; }
+
+.fa-people-roof::before {
+  content: "\e537"; }
+
+.fa-people-line::before {
+  content: "\e534"; }
+
+.fa-beer-mug-empty::before {
+  content: "\f0fc"; }
+
+.fa-beer::before {
+  content: "\f0fc"; }
+
+.fa-diagram-predecessor::before {
+  content: "\e477"; }
+
+.fa-arrow-up-long::before {
+  content: "\f176"; }
+
+.fa-long-arrow-up::before {
+  content: "\f176"; }
+
+.fa-fire-flame-simple::before {
+  content: "\f46a"; }
+
+.fa-burn::before {
+  content: "\f46a"; }
+
+.fa-person::before {
+  content: "\f183"; }
+
+.fa-male::before {
+  content: "\f183"; }
+
+.fa-laptop::before {
+  content: "\f109"; }
+
+.fa-file-csv::before {
+  content: "\f6dd"; }
+
+.fa-menorah::before {
+  content: "\f676"; }
+
+.fa-truck-plane::before {
+  content: "\e58f"; }
+
+.fa-record-vinyl::before {
+  content: "\f8d9"; }
+
+.fa-face-grin-stars::before {
+  content: "\f587"; }
+
+.fa-grin-stars::before {
+  content: "\f587"; }
+
+.fa-bong::before {
+  content: "\f55c"; }
+
+.fa-spaghetti-monster-flying::before {
+  content: "\f67b"; }
+
+.fa-pastafarianism::before {
+  content: "\f67b"; }
+
+.fa-arrow-down-up-across-line::before {
+  content: "\e4af"; }
+
+.fa-spoon::before {
+  content: "\f2e5"; }
+
+.fa-utensil-spoon::before {
+  content: "\f2e5"; }
+
+.fa-jar-wheat::before {
+  content: "\e517"; }
+
+.fa-envelopes-bulk::before {
+  content: "\f674"; }
+
+.fa-mail-bulk::before {
+  content: "\f674"; }
+
+.fa-file-circle-exclamation::before {
+  content: "\e4eb"; }
+
+.fa-circle-h::before {
+  content: "\f47e"; }
+
+.fa-hospital-symbol::before {
+  content: "\f47e"; }
+
+.fa-pager::before {
+  content: "\f815"; }
+
+.fa-address-book::before {
+  content: "\f2b9"; }
+
+.fa-contact-book::before {
+  content: "\f2b9"; }
+
+.fa-strikethrough::before {
+  content: "\f0cc"; }
+
+.fa-k::before {
+  content: "\4b"; }
+
+.fa-landmark-flag::before {
+  content: "\e51c"; }
+
+.fa-pencil::before {
+  content: "\f303"; }
+
+.fa-pencil-alt::before {
+  content: "\f303"; }
+
+.fa-backward::before {
+  content: "\f04a"; }
+
+.fa-caret-right::before {
+  content: "\f0da"; }
+
+.fa-comments::before {
+  content: "\f086"; }
+
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+  content: "\f0ea"; }
+
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+  content: "\f0ea"; }
+
+.fa-code-pull-request::before {
+  content: "\e13c"; }
+
+.fa-clipboard-list::before {
+  content: "\f46d"; }
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+.fa-truck-ramp-box::before {
+  content: "\f4de"; }
+
+.fa-truck-loading::before {
+  content: "\f4de"; }
+
+.fa-user-check::before {
+  content: "\f4fc"; }
+
+.fa-vial-virus::before {
+  content: "\e597"; }
+
+.fa-sheet-plastic::before {
+  content: "\e571"; }
+
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+  content: "\f781"; }
+
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+  content: "\f504"; }
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+  content: "\e539"; }
+
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+  content: "\f6a0"; }
+
+.fa-torah::before {
+  content: "\f6a0"; }
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+.fa-broom-ball::before {
+  content: "\f458"; }
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+  content: "\f458"; }
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+  content: "\f458"; }
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+  content: "\f204"; }
+
+.fa-box-archive::before {
+  content: "\f187"; }
+
+.fa-archive::before {
+  content: "\f187"; }
+
+.fa-person-drowning::before {
+  content: "\e545"; }
+
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+  content: "\f886"; }
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+  content: "\f886"; }
+
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+  content: "\f886"; }
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+  content: "\f58a"; }
+
+.fa-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-spray-can::before {
+  content: "\f5bd"; }
+
+.fa-truck-monster::before {
+  content: "\f63b"; }
+
+.fa-w::before {
+  content: "\57"; }
+
+.fa-earth-africa::before {
+  content: "\f57c"; }
+
+.fa-globe-africa::before {
+  content: "\f57c"; }
+
+.fa-rainbow::before {
+  content: "\f75b"; }
+
+.fa-circle-notch::before {
+  content: "\f1ce"; }
+
+.fa-tablet-screen-button::before {
+  content: "\f3fa"; }
+
+.fa-tablet-alt::before {
+  content: "\f3fa"; }
+
+.fa-paw::before {
+  content: "\f1b0"; }
+
+.fa-cloud::before {
+  content: "\f0c2"; }
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+.fa-trowel-bricks::before {
+  content: "\e58a"; }
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+.fa-face-flushed::before {
+  content: "\f579"; }
+
+.fa-flushed::before {
+  content: "\f579"; }
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+  content: "\f80d"; }
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+  content: "\e57f"; }
+
+.fa-gavel::before {
+  content: "\f0e3"; }
+
+.fa-legal::before {
+  content: "\f0e3"; }
+
+.fa-binoculars::before {
+  content: "\f1e5"; }
+
+.fa-microphone-slash::before {
+  content: "\f131"; }
+
+.fa-box-tissue::before {
+  content: "\e05b"; }
+
+.fa-motorcycle::before {
+  content: "\f21c"; }
+
+.fa-bell-concierge::before {
+  content: "\f562"; }
+
+.fa-concierge-bell::before {
+  content: "\f562"; }
+
+.fa-pen-ruler::before {
+  content: "\f5ae"; }
+
+.fa-pencil-ruler::before {
+  content: "\f5ae"; }
+
+.fa-people-arrows::before {
+  content: "\e068"; }
+
+.fa-people-arrows-left-right::before {
+  content: "\e068"; }
+
+.fa-mars-and-venus-burst::before {
+  content: "\e523"; }
+
+.fa-square-caret-right::before {
+  content: "\f152"; }
+
+.fa-caret-square-right::before {
+  content: "\f152"; }
+
+.fa-scissors::before {
+  content: "\f0c4"; }
+
+.fa-cut::before {
+  content: "\f0c4"; }
+
+.fa-sun-plant-wilt::before {
+  content: "\e57a"; }
+
+.fa-toilets-portable::before {
+  content: "\e584"; }
+
+.fa-hockey-puck::before {
+  content: "\f453"; }
+
+.fa-table::before {
+  content: "\f0ce"; }
+
+.fa-magnifying-glass-arrow-right::before {
+  content: "\e521"; }
+
+.fa-tachograph-digital::before {
+  content: "\f566"; }
+
+.fa-digital-tachograph::before {
+  content: "\f566"; }
+
+.fa-users-slash::before {
+  content: "\e073"; }
+
+.fa-clover::before {
+  content: "\e139"; }
+
+.fa-reply::before {
+  content: "\f3e5"; }
+
+.fa-mail-reply::before {
+  content: "\f3e5"; }
+
+.fa-star-and-crescent::before {
+  content: "\f699"; }
+
+.fa-house-fire::before {
+  content: "\e50c"; }
+
+.fa-square-minus::before {
+  content: "\f146"; }
+
+.fa-minus-square::before {
+  content: "\f146"; }
+
+.fa-helicopter::before {
+  content: "\f533"; }
+
+.fa-compass::before {
+  content: "\f14e"; }
+
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+  content: "\f150"; }
+
+.fa-caret-square-down::before {
+  content: "\f150"; }
+
+.fa-file-circle-question::before {
+  content: "\e4ef"; }
+
+.fa-laptop-code::before {
+  content: "\f5fc"; }
+
+.fa-swatchbook::before {
+  content: "\f5c3"; }
+
+.fa-prescription-bottle::before {
+  content: "\f485"; }
+
+.fa-bars::before {
+  content: "\f0c9"; }
+
+.fa-navicon::before {
+  content: "\f0c9"; }
+
+.fa-people-group::before {
+  content: "\e533"; }
+
+.fa-hourglass-end::before {
+  content: "\f253"; }
+
+.fa-hourglass-3::before {
+  content: "\f253"; }
+
+.fa-heart-crack::before {
+  content: "\f7a9"; }
+
+.fa-heart-broken::before {
+  content: "\f7a9"; }
+
+.fa-square-up-right::before {
+  content: "\f360"; }
+
+.fa-external-link-square-alt::before {
+  content: "\f360"; }
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+.fa-face-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-film::before {
+  content: "\f008"; }
+
+.fa-ruler-horizontal::before {
+  content: "\f547"; }
+
+.fa-people-robbery::before {
+  content: "\e536"; }
+
+.fa-lightbulb::before {
+  content: "\f0eb"; }
+
+.fa-caret-left::before {
+  content: "\f0d9"; }
+
+.fa-circle-exclamation::before {
+  content: "\f06a"; }
+
+.fa-exclamation-circle::before {
+  content: "\f06a"; }
+
+.fa-school-circle-xmark::before {
+  content: "\e56d"; }
+
+.fa-arrow-right-from-bracket::before {
+  content: "\f08b"; }
+
+.fa-sign-out::before {
+  content: "\f08b"; }
+
+.fa-circle-chevron-down::before {
+  content: "\f13a"; }
+
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+  content: "\f13a"; }
+
+.fa-unlock-keyhole::before {
+  content: "\f13e"; }
+
+.fa-unlock-alt::before {
+  content: "\f13e"; }
+
+.fa-cloud-showers-heavy::before {
+  content: "\f740"; }
+
+.fa-headphones-simple::before {
+  content: "\f58f"; }
+
+.fa-headphones-alt::before {
+  content: "\f58f"; }
+
+.fa-sitemap::before {
+  content: "\f0e8"; }
+
+.fa-circle-dollar-to-slot::before {
+  content: "\f4b9"; }
+
+.fa-donate::before {
+  content: "\f4b9"; }
+
+.fa-memory::before {
+  content: "\f538"; }
+
+.fa-road-spikes::before {
+  content: "\e568"; }
+
+.fa-fire-burner::before {
+  content: "\e4f1"; }
+
+.fa-flag::before {
+  content: "\f024"; }
+
+.fa-hanukiah::before {
+  content: "\f6e6"; }
+
+.fa-feather::before {
+  content: "\f52d"; }
+
+.fa-volume-low::before {
+  content: "\f027"; }
+
+.fa-volume-down::before {
+  content: "\f027"; }
+
+.fa-comment-slash::before {
+  content: "\f4b3"; }
+
+.fa-cloud-sun-rain::before {
+  content: "\f743"; }
+
+.fa-compress::before {
+  content: "\f066"; }
+
+.fa-wheat-awn::before {
+  content: "\e2cd"; }
+
+.fa-wheat-alt::before {
+  content: "\e2cd"; }
+
+.fa-ankh::before {
+  content: "\f644"; }
+
+.fa-hands-holding-child::before {
+  content: "\e4fa"; }
+
+.fa-asterisk::before {
+  content: "\2a"; }
+
+.fa-square-check::before {
+  content: "\f14a"; }
+
+.fa-check-square::before {
+  content: "\f14a"; }
+
+.fa-peseta-sign::before {
+  content: "\e221"; }
+
+.fa-heading::before {
+  content: "\f1dc"; }
+
+.fa-header::before {
+  content: "\f1dc"; }
+
+.fa-ghost::before {
+  content: "\f6e2"; }
+
+.fa-list::before {
+  content: "\f03a"; }
+
+.fa-list-squares::before {
+  content: "\f03a"; }
+
+.fa-square-phone-flip::before {
+  content: "\f87b"; }
+
+.fa-phone-square-alt::before {
+  content: "\f87b"; }
+
+.fa-cart-plus::before {
+  content: "\f217"; }
+
+.fa-gamepad::before {
+  content: "\f11b"; }
+
+.fa-circle-dot::before {
+  content: "\f192"; }
+
+.fa-dot-circle::before {
+  content: "\f192"; }
+
+.fa-face-dizzy::before {
+  content: "\f567"; }
+
+.fa-dizzy::before {
+  content: "\f567"; }
+
+.fa-egg::before {
+  content: "\f7fb"; }
+
+.fa-house-medical-circle-xmark::before {
+  content: "\e513"; }
+
+.fa-campground::before {
+  content: "\f6bb"; }
+
+.fa-folder-plus::before {
+  content: "\f65e"; }
+
+.fa-futbol::before {
+  content: "\f1e3"; }
+
+.fa-futbol-ball::before {
+  content: "\f1e3"; }
+
+.fa-soccer-ball::before {
+  content: "\f1e3"; }
+
+.fa-paintbrush::before {
+  content: "\f1fc"; }
+
+.fa-paint-brush::before {
+  content: "\f1fc"; }
+
+.fa-lock::before {
+  content: "\f023"; }
+
+.fa-gas-pump::before {
+  content: "\f52f"; }
+
+.fa-hot-tub-person::before {
+  content: "\f593"; }
+
+.fa-hot-tub::before {
+  content: "\f593"; }
+
+.fa-map-location::before {
+  content: "\f59f"; }
+
+.fa-map-marked::before {
+  content: "\f59f"; }
+
+.fa-house-flood-water::before {
+  content: "\e50e"; }
+
+.fa-tree::before {
+  content: "\f1bb"; }
+
+.fa-bridge-lock::before {
+  content: "\e4cc"; }
+
+.fa-sack-dollar::before {
+  content: "\f81d"; }
+
+.fa-pen-to-square::before {
+  content: "\f044"; }
+
+.fa-edit::before {
+  content: "\f044"; }
+
+.fa-car-side::before {
+  content: "\f5e4"; }
+
+.fa-share-nodes::before {
+  content: "\f1e0"; }
+
+.fa-share-alt::before {
+  content: "\f1e0"; }
+
+.fa-heart-circle-minus::before {
+  content: "\e4ff"; }
+
+.fa-hourglass-half::before {
+  content: "\f252"; }
+
+.fa-hourglass-2::before {
+  content: "\f252"; }
+
+.fa-microscope::before {
+  content: "\f610"; }
+
+.fa-sink::before {
+  content: "\e06d"; }
+
+.fa-bag-shopping::before {
+  content: "\f290"; }
+
+.fa-shopping-bag::before {
+  content: "\f290"; }
+
+.fa-arrow-down-z-a::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-desc::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-down-alt::before {
+  content: "\f881"; }
+
+.fa-mitten::before {
+  content: "\f7b5"; }
+
+.fa-person-rays::before {
+  content: "\e54d"; }
+
+.fa-users::before {
+  content: "\f0c0"; }
+
+.fa-eye-slash::before {
+  content: "\f070"; }
+
+.fa-flask-vial::before {
+  content: "\e4f3"; }
+
+.fa-hand::before {
+  content: "\f256"; }
+
+.fa-hand-paper::before {
+  content: "\f256"; }
+
+.fa-om::before {
+  content: "\f679"; }
+
+.fa-worm::before {
+  content: "\e599"; }
+
+.fa-house-circle-xmark::before {
+  content: "\e50b"; }
+
+.fa-plug::before {
+  content: "\f1e6"; }
+
+.fa-chevron-up::before {
+  content: "\f077"; }
+
+.fa-hand-spock::before {
+  content: "\f259"; }
+
+.fa-stopwatch::before {
+  content: "\f2f2"; }
+
+.fa-face-kiss::before {
+  content: "\f596"; }
+
+.fa-kiss::before {
+  content: "\f596"; }
+
+.fa-bridge-circle-xmark::before {
+  content: "\e4cb"; }
+
+.fa-face-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-chess-bishop::before {
+  content: "\f43a"; }
+
+.fa-face-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-ear-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deafness::before {
+  content: "\f2a4"; }
+
+.fa-hard-of-hearing::before {
+  content: "\f2a4"; }
+
+.fa-road-circle-check::before {
+  content: "\e564"; }
+
+.fa-dice-five::before {
+  content: "\f523"; }
+
+.fa-square-rss::before {
+  content: "\f143"; }
+
+.fa-rss-square::before {
+  content: "\f143"; }
+
+.fa-land-mine-on::before {
+  content: "\e51b"; }
+
+.fa-i-cursor::before {
+  content: "\f246"; }
+
+.fa-stamp::before {
+  content: "\f5bf"; }
+
+.fa-stairs::before {
+  content: "\e289"; }
+
+.fa-i::before {
+  content: "\49"; }
+
+.fa-hryvnia-sign::before {
+  content: "\f6f2"; }
+
+.fa-hryvnia::before {
+  content: "\f6f2"; }
+
+.fa-pills::before {
+  content: "\f484"; }
+
+.fa-face-grin-wide::before {
+  content: "\f581"; }
+
+.fa-grin-alt::before {
+  content: "\f581"; }
+
+.fa-tooth::before {
+  content: "\f5c9"; }
+
+.fa-v::before {
+  content: "\56"; }
+
+.fa-bangladeshi-taka-sign::before {
+  content: "\e2e6"; }
+
+.fa-bicycle::before {
+  content: "\f206"; }
+
+.fa-staff-snake::before {
+  content: "\e579"; }
+
+.fa-rod-asclepius::before {
+  content: "\e579"; }
+
+.fa-rod-snake::before {
+  content: "\e579"; }
+
+.fa-staff-aesculapius::before {
+  content: "\e579"; }
+
+.fa-head-side-cough-slash::before {
+  content: "\e062"; }
+
+.fa-truck-medical::before {
+  content: "\f0f9"; }
+
+.fa-ambulance::before {
+  content: "\f0f9"; }
+
+.fa-wheat-awn-circle-exclamation::before {
+  content: "\e598"; }
+
+.fa-snowman::before {
+  content: "\f7d0"; }
+
+.fa-mortar-pestle::before {
+  content: "\f5a7"; }
+
+.fa-road-barrier::before {
+  content: "\e562"; }
+
+.fa-school::before {
+  content: "\f549"; }
+
+.fa-igloo::before {
+  content: "\f7ae"; }
+
+.fa-joint::before {
+  content: "\f595"; }
+
+.fa-angle-right::before {
+  content: "\f105"; }
+
+.fa-horse::before {
+  content: "\f6f0"; }
+
+.fa-q::before {
+  content: "\51"; }
+
+.fa-g::before {
+  content: "\47"; }
+
+.fa-notes-medical::before {
+  content: "\f481"; }
+
+.fa-temperature-half::before {
+  content: "\f2c9"; }
+
+.fa-temperature-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-half::before {
+  content: "\f2c9"; }
+
+.fa-dong-sign::before {
+  content: "\e169"; }
+
+.fa-capsules::before {
+  content: "\f46b"; }
+
+.fa-poo-storm::before {
+  content: "\f75a"; }
+
+.fa-poo-bolt::before {
+  content: "\f75a"; }
+
+.fa-face-frown-open::before {
+  content: "\f57a"; }
+
+.fa-frown-open::before {
+  content: "\f57a"; }
+
+.fa-hand-point-up::before {
+  content: "\f0a6"; }
+
+.fa-money-bill::before {
+  content: "\f0d6"; }
+
+.fa-bookmark::before {
+  content: "\f02e"; }
+
+.fa-align-justify::before {
+  content: "\f039"; }
+
+.fa-umbrella-beach::before {
+  content: "\f5ca"; }
+
+.fa-helmet-un::before {
+  content: "\e503"; }
+
+.fa-bullseye::before {
+  content: "\f140"; }
+
+.fa-bacon::before {
+  content: "\f7e5"; }
+
+.fa-hand-point-down::before {
+  content: "\f0a7"; }
+
+.fa-arrow-up-from-bracket::before {
+  content: "\e09a"; }
+
+.fa-folder::before {
+  content: "\f07b"; }
+
+.fa-folder-blank::before {
+  content: "\f07b"; }
+
+.fa-file-waveform::before {
+  content: "\f478"; }
+
+.fa-file-medical-alt::before {
+  content: "\f478"; }
+
+.fa-radiation::before {
+  content: "\f7b9"; }
+
+.fa-chart-simple::before {
+  content: "\e473"; }
+
+.fa-mars-stroke::before {
+  content: "\f229"; }
+
+.fa-vial::before {
+  content: "\f492"; }
+
+.fa-gauge::before {
+  content: "\f624"; }
+
+.fa-dashboard::before {
+  content: "\f624"; }
+
+.fa-gauge-med::before {
+  content: "\f624"; }
+
+.fa-tachometer-alt-average::before {
+  content: "\f624"; }
+
+.fa-wand-magic-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-magic-wand-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-e::before {
+  content: "\45"; }
+
+.fa-pen-clip::before {
+  content: "\f305"; }
+
+.fa-pen-alt::before {
+  content: "\f305"; }
+
+.fa-bridge-circle-exclamation::before {
+  content: "\e4ca"; }
+
+.fa-user::before {
+  content: "\f007"; }
+
+.fa-school-circle-check::before {
+  content: "\e56b"; }
+
+.fa-dumpster::before {
+  content: "\f793"; }
+
+.fa-van-shuttle::before {
+  content: "\f5b6"; }
+
+.fa-shuttle-van::before {
+  content: "\f5b6"; }
+
+.fa-building-user::before {
+  content: "\e4da"; }
+
+.fa-square-caret-left::before {
+  content: "\f191"; }
+
+.fa-caret-square-left::before {
+  content: "\f191"; }
+
+.fa-highlighter::before {
+  content: "\f591"; }
+
+.fa-key::before {
+  content: "\f084"; }
+
+.fa-bullhorn::before {
+  content: "\f0a1"; }
+
+.fa-globe::before {
+  content: "\f0ac"; }
+
+.fa-synagogue::before {
+  content: "\f69b"; }
+
+.fa-person-half-dress::before {
+  content: "\e548"; }
+
+.fa-road-bridge::before {
+  content: "\e563"; }
+
+.fa-location-arrow::before {
+  content: "\f124"; }
+
+.fa-c::before {
+  content: "\43"; }
+
+.fa-tablet-button::before {
+  content: "\f10a"; }
+
+.fa-building-lock::before {
+  content: "\e4d6"; }
+
+.fa-pizza-slice::before {
+  content: "\f818"; }
+
+.fa-money-bill-wave::before {
+  content: "\f53a"; }
+
+.fa-chart-area::before {
+  content: "\f1fe"; }
+
+.fa-area-chart::before {
+  content: "\f1fe"; }
+
+.fa-house-flag::before {
+  content: "\e50d"; }
+
+.fa-person-circle-minus::before {
+  content: "\e540"; }
+
+.fa-ban::before {
+  content: "\f05e"; }
+
+.fa-cancel::before {
+  content: "\f05e"; }
+
+.fa-camera-rotate::before {
+  content: "\e0d8"; }
+
+.fa-spray-can-sparkles::before {
+  content: "\f5d0"; }
+
+.fa-air-freshener::before {
+  content: "\f5d0"; }
+
+.fa-star::before {
+  content: "\f005"; }
+
+.fa-repeat::before {
+  content: "\f363"; }
+
+.fa-cross::before {
+  content: "\f654"; }
+
+.fa-box::before {
+  content: "\f466"; }
+
+.fa-venus-mars::before {
+  content: "\f228"; }
+
+.fa-arrow-pointer::before {
+  content: "\f245"; }
+
+.fa-mouse-pointer::before {
+  content: "\f245"; }
+
+.fa-maximize::before {
+  content: "\f31e"; }
+
+.fa-expand-arrows-alt::before {
+  content: "\f31e"; }
+
+.fa-charging-station::before {
+  content: "\f5e7"; }
+
+.fa-shapes::before {
+  content: "\f61f"; }
+
+.fa-triangle-circle-square::before {
+  content: "\f61f"; }
+
+.fa-shuffle::before {
+  content: "\f074"; }
+
+.fa-random::before {
+  content: "\f074"; }
+
+.fa-person-running::before {
+  content: "\f70c"; }
+
+.fa-running::before {
+  content: "\f70c"; }
+
+.fa-mobile-retro::before {
+  content: "\e527"; }
+
+.fa-grip-lines-vertical::before {
+  content: "\f7a5"; }
+
+.fa-spider::before {
+  content: "\f717"; }
+
+.fa-hands-bound::before {
+  content: "\e4f9"; }
+
+.fa-file-invoice-dollar::before {
+  content: "\f571"; }
+
+.fa-plane-circle-exclamation::before {
+  content: "\e556"; }
+
+.fa-x-ray::before {
+  content: "\f497"; }
+
+.fa-spell-check::before {
+  content: "\f891"; }
+
+.fa-slash::before {
+  content: "\f715"; }
+
+.fa-computer-mouse::before {
+  content: "\f8cc"; }
+
+.fa-mouse::before {
+  content: "\f8cc"; }
+
+.fa-arrow-right-to-bracket::before {
+  content: "\f090"; }
+
+.fa-sign-in::before {
+  content: "\f090"; }
+
+.fa-shop-slash::before {
+  content: "\e070"; }
+
+.fa-store-alt-slash::before {
+  content: "\e070"; }
+
+.fa-server::before {
+  content: "\f233"; }
+
+.fa-virus-covid-slash::before {
+  content: "\e4a9"; }
+
+.fa-shop-lock::before {
+  content: "\e4a5"; }
+
+.fa-hourglass-start::before {
+  content: "\f251"; }
+
+.fa-hourglass-1::before {
+  content: "\f251"; }
+
+.fa-blender-phone::before {
+  content: "\f6b6"; }
+
+.fa-building-wheat::before {
+  content: "\e4db"; }
+
+.fa-person-breastfeeding::before {
+  content: "\e53a"; }
+
+.fa-right-to-bracket::before {
+  content: "\f2f6"; }
+
+.fa-sign-in-alt::before {
+  content: "\f2f6"; }
+
+.fa-venus::before {
+  content: "\f221"; }
+
+.fa-passport::before {
+  content: "\f5ab"; }
+
+.fa-heart-pulse::before {
+  content: "\f21e"; }
+
+.fa-heartbeat::before {
+  content: "\f21e"; }
+
+.fa-people-carry-box::before {
+  content: "\f4ce"; }
+
+.fa-people-carry::before {
+  content: "\f4ce"; }
+
+.fa-temperature-high::before {
+  content: "\f769"; }
+
+.fa-microchip::before {
+  content: "\f2db"; }
+
+.fa-crown::before {
+  content: "\f521"; }
+
+.fa-weight-hanging::before {
+  content: "\f5cd"; }
+
+.fa-xmarks-lines::before {
+  content: "\e59a"; }
+
+.fa-file-prescription::before {
+  content: "\f572"; }
+
+.fa-weight-scale::before {
+  content: "\f496"; }
+
+.fa-weight::before {
+  content: "\f496"; }
+
+.fa-user-group::before {
+  content: "\f500"; }
+
+.fa-user-friends::before {
+  content: "\f500"; }
+
+.fa-arrow-up-a-z::before {
+  content: "\f15e"; }
+
+.fa-sort-alpha-up::before {
+  content: "\f15e"; }
+
+.fa-chess-knight::before {
+  content: "\f441"; }
+
+.fa-face-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-wheelchair::before {
+  content: "\f193"; }
+
+.fa-circle-arrow-up::before {
+  content: "\f0aa"; }
+
+.fa-arrow-circle-up::before {
+  content: "\f0aa"; }
+
+.fa-toggle-on::before {
+  content: "\f205"; }
+
+.fa-person-walking::before {
+  content: "\f554"; }
+
+.fa-walking::before {
+  content: "\f554"; }
+
+.fa-l::before {
+  content: "\4c"; }
+
+.fa-fire::before {
+  content: "\f06d"; }
+
+.fa-bed-pulse::before {
+  content: "\f487"; }
+
+.fa-procedures::before {
+  content: "\f487"; }
+
+.fa-shuttle-space::before {
+  content: "\f197"; }
+
+.fa-space-shuttle::before {
+  content: "\f197"; }
+
+.fa-face-laugh::before {
+  content: "\f599"; }
+
+.fa-laugh::before {
+  content: "\f599"; }
+
+.fa-folder-open::before {
+  content: "\f07c"; }
+
+.fa-heart-circle-plus::before {
+  content: "\e500"; }
+
+.fa-code-fork::before {
+  content: "\e13b"; }
+
+.fa-city::before {
+  content: "\f64f"; }
+
+.fa-microphone-lines::before {
+  content: "\f3c9"; }
+
+.fa-microphone-alt::before {
+  content: "\f3c9"; }
+
+.fa-pepper-hot::before {
+  content: "\f816"; }
+
+.fa-unlock::before {
+  content: "\f09c"; }
+
+.fa-colon-sign::before {
+  content: "\e140"; }
+
+.fa-headset::before {
+  content: "\f590"; }
+
+.fa-store-slash::before {
+  content: "\e071"; }
+
+.fa-road-circle-xmark::before {
+  content: "\e566"; }
+
+.fa-user-minus::before {
+  content: "\f503"; }
+
+.fa-mars-stroke-up::before {
+  content: "\f22a"; }
+
+.fa-mars-stroke-v::before {
+  content: "\f22a"; }
+
+.fa-champagne-glasses::before {
+  content: "\f79f"; }
+
+.fa-glass-cheers::before {
+  content: "\f79f"; }
+
+.fa-clipboard::before {
+  content: "\f328"; }
+
+.fa-house-circle-exclamation::before {
+  content: "\e50a"; }
+
+.fa-file-arrow-up::before {
+  content: "\f574"; }
+
+.fa-file-upload::before {
+  content: "\f574"; }
+
+.fa-wifi::before {
+  content: "\f1eb"; }
+
+.fa-wifi-3::before {
+  content: "\f1eb"; }
+
+.fa-wifi-strong::before {
+  content: "\f1eb"; }
+
+.fa-bath::before {
+  content: "\f2cd"; }
+
+.fa-bathtub::before {
+  content: "\f2cd"; }
+
+.fa-underline::before {
+  content: "\f0cd"; }
+
+.fa-user-pen::before {
+  content: "\f4ff"; }
+
+.fa-user-edit::before {
+  content: "\f4ff"; }
+
+.fa-signature::before {
+  content: "\f5b7"; }
+
+.fa-stroopwafel::before {
+  content: "\f551"; }
+
+.fa-bold::before {
+  content: "\f032"; }
+
+.fa-anchor-lock::before {
+  content: "\e4ad"; }
+
+.fa-building-ngo::before {
+  content: "\e4d7"; }
+
+.fa-manat-sign::before {
+  content: "\e1d5"; }
+
+.fa-not-equal::before {
+  content: "\f53e"; }
+
+.fa-border-top-left::before {
+  content: "\f853"; }
+
+.fa-border-style::before {
+  content: "\f853"; }
+
+.fa-map-location-dot::before {
+  content: "\f5a0"; }
+
+.fa-map-marked-alt::before {
+  content: "\f5a0"; }
+
+.fa-jedi::before {
+  content: "\f669"; }
+
+.fa-square-poll-vertical::before {
+  content: "\f681"; }
+
+.fa-poll::before {
+  content: "\f681"; }
+
+.fa-mug-hot::before {
+  content: "\f7b6"; }
+
+.fa-car-battery::before {
+  content: "\f5df"; }
+
+.fa-battery-car::before {
+  content: "\f5df"; }
+
+.fa-gift::before {
+  content: "\f06b"; }
+
+.fa-dice-two::before {
+  content: "\f528"; }
+
+.fa-chess-queen::before {
+  content: "\f445"; }
+
+.fa-glasses::before {
+  content: "\f530"; }
+
+.fa-chess-board::before {
+  content: "\f43c"; }
+
+.fa-building-circle-check::before {
+  content: "\e4d2"; }
+
+.fa-person-chalkboard::before {
+  content: "\e53d"; }
+
+.fa-mars-stroke-right::before {
+  content: "\f22b"; }
+
+.fa-mars-stroke-h::before {
+  content: "\f22b"; }
+
+.fa-hand-back-fist::before {
+  content: "\f255"; }
+
+.fa-hand-rock::before {
+  content: "\f255"; }
+
+.fa-square-caret-up::before {
+  content: "\f151"; }
+
+.fa-caret-square-up::before {
+  content: "\f151"; }
+
+.fa-cloud-showers-water::before {
+  content: "\e4e4"; }
+
+.fa-chart-bar::before {
+  content: "\f080"; }
+
+.fa-bar-chart::before {
+  content: "\f080"; }
+
+.fa-hands-bubbles::before {
+  content: "\e05e"; }
+
+.fa-hands-wash::before {
+  content: "\e05e"; }
+
+.fa-less-than-equal::before {
+  content: "\f537"; }
+
+.fa-train::before {
+  content: "\f238"; }
+
+.fa-eye-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-crow::before {
+  content: "\f520"; }
+
+.fa-sailboat::before {
+  content: "\e445"; }
+
+.fa-window-restore::before {
+  content: "\f2d2"; }
+
+.fa-square-plus::before {
+  content: "\f0fe"; }
+
+.fa-plus-square::before {
+  content: "\f0fe"; }
+
+.fa-torii-gate::before {
+  content: "\f6a1"; }
+
+.fa-frog::before {
+  content: "\f52e"; }
+
+.fa-bucket::before {
+  content: "\e4cf"; }
+
+.fa-image::before {
+  content: "\f03e"; }
+
+.fa-microphone::before {
+  content: "\f130"; }
+
+.fa-cow::before {
+  content: "\f6c8"; }
+
+.fa-caret-up::before {
+  content: "\f0d8"; }
+
+.fa-screwdriver::before {
+  content: "\f54a"; }
+
+.fa-folder-closed::before {
+  content: "\e185"; }
+
+.fa-house-tsunami::before {
+  content: "\e515"; }
+
+.fa-square-nfi::before {
+  content: "\e576"; }
+
+.fa-arrow-up-from-ground-water::before {
+  content: "\e4b5"; }
+
+.fa-martini-glass::before {
+  content: "\f57b"; }
+
+.fa-glass-martini-alt::before {
+  content: "\f57b"; }
+
+.fa-rotate-left::before {
+  content: "\f2ea"; }
+
+.fa-rotate-back::before {
+  content: "\f2ea"; }
+
+.fa-rotate-backward::before {
+  content: "\f2ea"; }
+
+.fa-undo-alt::before {
+  content: "\f2ea"; }
+
+.fa-table-columns::before {
+  content: "\f0db"; }
+
+.fa-columns::before {
+  content: "\f0db"; }
+
+.fa-lemon::before {
+  content: "\f094"; }
+
+.fa-head-side-mask::before {
+  content: "\e063"; }
+
+.fa-handshake::before {
+  content: "\f2b5"; }
+
+.fa-gem::before {
+  content: "\f3a5"; }
+
+.fa-dolly::before {
+  content: "\f472"; }
+
+.fa-dolly-box::before {
+  content: "\f472"; }
+
+.fa-smoking::before {
+  content: "\f48d"; }
+
+.fa-minimize::before {
+  content: "\f78c"; }
+
+.fa-compress-arrows-alt::before {
+  content: "\f78c"; }
+
+.fa-monument::before {
+  content: "\f5a6"; }
+
+.fa-snowplow::before {
+  content: "\f7d2"; }
+
+.fa-angles-right::before {
+  content: "\f101"; }
+
+.fa-angle-double-right::before {
+  content: "\f101"; }
+
+.fa-cannabis::before {
+  content: "\f55f"; }
+
+.fa-circle-play::before {
+  content: "\f144"; }
+
+.fa-play-circle::before {
+  content: "\f144"; }
+
+.fa-tablets::before {
+  content: "\f490"; }
+
+.fa-ethernet::before {
+  content: "\f796"; }
+
+.fa-euro-sign::before {
+  content: "\f153"; }
+
+.fa-eur::before {
+  content: "\f153"; }
+
+.fa-euro::before {
+  content: "\f153"; }
+
+.fa-chair::before {
+  content: "\f6c0"; }
+
+.fa-circle-check::before {
+  content: "\f058"; }
+
+.fa-check-circle::before {
+  content: "\f058"; }
+
+.fa-circle-stop::before {
+  content: "\f28d"; }
+
+.fa-stop-circle::before {
+  content: "\f28d"; }
+
+.fa-compass-drafting::before {
+  content: "\f568"; }
+
+.fa-drafting-compass::before {
+  content: "\f568"; }
+
+.fa-plate-wheat::before {
+  content: "\e55a"; }
+
+.fa-icicles::before {
+  content: "\f7ad"; }
+
+.fa-person-shelter::before {
+  content: "\e54f"; }
+
+.fa-neuter::before {
+  content: "\f22c"; }
+
+.fa-id-badge::before {
+  content: "\f2c1"; }
+
+.fa-marker::before {
+  content: "\f5a1"; }
+
+.fa-face-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-helicopter-symbol::before {
+  content: "\e502"; }
+
+.fa-universal-access::before {
+  content: "\f29a"; }
+
+.fa-circle-chevron-up::before {
+  content: "\f139"; }
+
+.fa-chevron-circle-up::before {
+  content: "\f139"; }
+
+.fa-lari-sign::before {
+  content: "\e1c8"; }
+
+.fa-volcano::before {
+  content: "\f770"; }
+
+.fa-person-walking-dashed-line-arrow-right::before {
+  content: "\e553"; }
+
+.fa-sterling-sign::before {
+  content: "\f154"; }
+
+.fa-gbp::before {
+  content: "\f154"; }
+
+.fa-pound-sign::before {
+  content: "\f154"; }
+
+.fa-viruses::before {
+  content: "\e076"; }
+
+.fa-square-person-confined::before {
+  content: "\e577"; }
+
+.fa-user-tie::before {
+  content: "\f508"; }
+
+.fa-arrow-down-long::before {
+  content: "\f175"; }
+
+.fa-long-arrow-down::before {
+  content: "\f175"; }
+
+.fa-tent-arrow-down-to-line::before {
+  content: "\e57e"; }
+
+.fa-certificate::before {
+  content: "\f0a3"; }
+
+.fa-reply-all::before {
+  content: "\f122"; }
+
+.fa-mail-reply-all::before {
+  content: "\f122"; }
+
+.fa-suitcase::before {
+  content: "\f0f2"; }
+
+.fa-person-skating::before {
+  content: "\f7c5"; }
+
+.fa-skating::before {
+  content: "\f7c5"; }
+
+.fa-filter-circle-dollar::before {
+  content: "\f662"; }
+
+.fa-funnel-dollar::before {
+  content: "\f662"; }
+
+.fa-camera-retro::before {
+  content: "\f083"; }
+
+.fa-circle-arrow-down::before {
+  content: "\f0ab"; }
+
+.fa-arrow-circle-down::before {
+  content: "\f0ab"; }
+
+.fa-file-import::before {
+  content: "\f56f"; }
+
+.fa-arrow-right-to-file::before {
+  content: "\f56f"; }
+
+.fa-square-arrow-up-right::before {
+  content: "\f14c"; }
+
+.fa-external-link-square::before {
+  content: "\f14c"; }
+
+.fa-box-open::before {
+  content: "\f49e"; }
+
+.fa-scroll::before {
+  content: "\f70e"; }
+
+.fa-spa::before {
+  content: "\f5bb"; }
+
+.fa-location-pin-lock::before {
+  content: "\e51f"; }
+
+.fa-pause::before {
+  content: "\f04c"; }
+
+.fa-hill-avalanche::before {
+  content: "\e507"; }
+
+.fa-temperature-empty::before {
+  content: "\f2cb"; }
+
+.fa-temperature-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-empty::before {
+  content: "\f2cb"; }
+
+.fa-bomb::before {
+  content: "\f1e2"; }
+
+.fa-registered::before {
+  content: "\f25d"; }
+
+.fa-address-card::before {
+  content: "\f2bb"; }
+
+.fa-contact-card::before {
+  content: "\f2bb"; }
+
+.fa-vcard::before {
+  content: "\f2bb"; }
+
+.fa-scale-unbalanced-flip::before {
+  content: "\f516"; }
+
+.fa-balance-scale-right::before {
+  content: "\f516"; }
+
+.fa-subscript::before {
+  content: "\f12c"; }
+
+.fa-diamond-turn-right::before {
+  content: "\f5eb"; }
+
+.fa-directions::before {
+  content: "\f5eb"; }
+
+.fa-burst::before {
+  content: "\e4dc"; }
+
+.fa-house-laptop::before {
+  content: "\e066"; }
+
+.fa-laptop-house::before {
+  content: "\e066"; }
+
+.fa-face-tired::before {
+  content: "\f5c8"; }
+
+.fa-tired::before {
+  content: "\f5c8"; }
+
+.fa-money-bills::before {
+  content: "\e1f3"; }
+
+.fa-smog::before {
+  content: "\f75f"; }
+
+.fa-crutch::before {
+  content: "\f7f7"; }
+
+.fa-cloud-arrow-up::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload-alt::before {
+  content: "\f0ee"; }
+
+.fa-palette::before {
+  content: "\f53f"; }
+
+.fa-arrows-turn-right::before {
+  content: "\e4c0"; }
+
+.fa-vest::before {
+  content: "\e085"; }
+
+.fa-ferry::before {
+  content: "\e4ea"; }
+
+.fa-arrows-down-to-people::before {
+  content: "\e4b9"; }
+
+.fa-seedling::before {
+  content: "\f4d8"; }
+
+.fa-sprout::before {
+  content: "\f4d8"; }
+
+.fa-left-right::before {
+  content: "\f337"; }
+
+.fa-arrows-alt-h::before {
+  content: "\f337"; }
+
+.fa-boxes-packing::before {
+  content: "\e4c7"; }
+
+.fa-circle-arrow-left::before {
+  content: "\f0a8"; }
+
+.fa-arrow-circle-left::before {
+  content: "\f0a8"; }
+
+.fa-group-arrows-rotate::before {
+  content: "\e4f6"; }
+
+.fa-bowl-food::before {
+  content: "\e4c6"; }
+
+.fa-candy-cane::before {
+  content: "\f786"; }
+
+.fa-arrow-down-wide-short::before {
+  content: "\f160"; }
+
+.fa-sort-amount-asc::before {
+  content: "\f160"; }
+
+.fa-sort-amount-down::before {
+  content: "\f160"; }
+
+.fa-cloud-bolt::before {
+  content: "\f76c"; }
+
+.fa-thunderstorm::before {
+  content: "\f76c"; }
+
+.fa-text-slash::before {
+  content: "\f87d"; }
+
+.fa-remove-format::before {
+  content: "\f87d"; }
+
+.fa-face-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-file-word::before {
+  content: "\f1c2"; }
+
+.fa-file-powerpoint::before {
+  content: "\f1c4"; }
+
+.fa-arrows-left-right::before {
+  content: "\f07e"; }
+
+.fa-arrows-h::before {
+  content: "\f07e"; }
+
+.fa-house-lock::before {
+  content: "\e510"; }
+
+.fa-cloud-arrow-down::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download-alt::before {
+  content: "\f0ed"; }
+
+.fa-children::before {
+  content: "\e4e1"; }
+
+.fa-chalkboard::before {
+  content: "\f51b"; }
+
+.fa-blackboard::before {
+  content: "\f51b"; }
+
+.fa-user-large-slash::before {
+  content: "\f4fa"; }
+
+.fa-user-alt-slash::before {
+  content: "\f4fa"; }
+
+.fa-envelope-open::before {
+  content: "\f2b6"; }
+
+.fa-handshake-simple-slash::before {
+  content: "\e05f"; }
+
+.fa-handshake-alt-slash::before {
+  content: "\e05f"; }
+
+.fa-mattress-pillow::before {
+  content: "\e525"; }
+
+.fa-guarani-sign::before {
+  content: "\e19a"; }
+
+.fa-arrows-rotate::before {
+  content: "\f021"; }
+
+.fa-refresh::before {
+  content: "\f021"; }
+
+.fa-sync::before {
+  content: "\f021"; }
+
+.fa-fire-extinguisher::before {
+  content: "\f134"; }
+
+.fa-cruzeiro-sign::before {
+  content: "\e152"; }
+
+.fa-greater-than-equal::before {
+  content: "\f532"; }
+
+.fa-shield-halved::before {
+  content: "\f3ed"; }
+
+.fa-shield-alt::before {
+  content: "\f3ed"; }
+
+.fa-book-atlas::before {
+  content: "\f558"; }
+
+.fa-atlas::before {
+  content: "\f558"; }
+
+.fa-virus::before {
+  content: "\e074"; }
+
+.fa-envelope-circle-check::before {
+  content: "\e4e8"; }
+
+.fa-layer-group::before {
+  content: "\f5fd"; }
+
+.fa-arrows-to-dot::before {
+  content: "\e4be"; }
+
+.fa-archway::before {
+  content: "\f557"; }
+
+.fa-heart-circle-check::before {
+  content: "\e4fd"; }
+
+.fa-house-chimney-crack::before {
+  content: "\f6f1"; }
+
+.fa-house-damage::before {
+  content: "\f6f1"; }
+
+.fa-file-zipper::before {
+  content: "\f1c6"; }
+
+.fa-file-archive::before {
+  content: "\f1c6"; }
+
+.fa-square::before {
+  content: "\f0c8"; }
+
+.fa-martini-glass-empty::before {
+  content: "\f000"; }
+
+.fa-glass-martini::before {
+  content: "\f000"; }
+
+.fa-couch::before {
+  content: "\f4b8"; }
+
+.fa-cedi-sign::before {
+  content: "\e0df"; }
+
+.fa-italic::before {
+  content: "\f033"; }
+
+.fa-church::before {
+  content: "\f51d"; }
+
+.fa-comments-dollar::before {
+  content: "\f653"; }
+
+.fa-democrat::before {
+  content: "\f747"; }
+
+.fa-z::before {
+  content: "\5a"; }
+
+.fa-person-skiing::before {
+  content: "\f7c9"; }
+
+.fa-skiing::before {
+  content: "\f7c9"; }
+
+.fa-road-lock::before {
+  content: "\e567"; }
+
+.fa-a::before {
+  content: "\41"; }
+
+.fa-temperature-arrow-down::before {
+  content: "\e03f"; }
+
+.fa-temperature-down::before {
+  content: "\e03f"; }
+
+.fa-feather-pointed::before {
+  content: "\f56b"; }
+
+.fa-feather-alt::before {
+  content: "\f56b"; }
+
+.fa-p::before {
+  content: "\50"; }
+
+.fa-snowflake::before {
+  content: "\f2dc"; }
+
+.fa-newspaper::before {
+  content: "\f1ea"; }
+
+.fa-rectangle-ad::before {
+  content: "\f641"; }
+
+.fa-ad::before {
+  content: "\f641"; }
+
+.fa-circle-arrow-right::before {
+  content: "\f0a9"; }
+
+.fa-arrow-circle-right::before {
+  content: "\f0a9"; }
+
+.fa-filter-circle-xmark::before {
+  content: "\e17b"; }
+
+.fa-locust::before {
+  content: "\e520"; }
+
+.fa-sort::before {
+  content: "\f0dc"; }
+
+.fa-unsorted::before {
+  content: "\f0dc"; }
+
+.fa-list-ol::before {
+  content: "\f0cb"; }
+
+.fa-list-1-2::before {
+  content: "\f0cb"; }
+
+.fa-list-numeric::before {
+  content: "\f0cb"; }
+
+.fa-person-dress-burst::before {
+  content: "\e544"; }
+
+.fa-money-check-dollar::before {
+  content: "\f53d"; }
+
+.fa-money-check-alt::before {
+  content: "\f53d"; }
+
+.fa-vector-square::before {
+  content: "\f5cb"; }
+
+.fa-bread-slice::before {
+  content: "\f7ec"; }
+
+.fa-language::before {
+  content: "\f1ab"; }
+
+.fa-face-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-filter::before {
+  content: "\f0b0"; }
+
+.fa-question::before {
+  content: "\3f"; }
+
+.fa-file-signature::before {
+  content: "\f573"; }
+
+.fa-up-down-left-right::before {
+  content: "\f0b2"; }
+
+.fa-arrows-alt::before {
+  content: "\f0b2"; }
+
+.fa-house-chimney-user::before {
+  content: "\e065"; }
+
+.fa-hand-holding-heart::before {
+  content: "\f4be"; }
+
+.fa-puzzle-piece::before {
+  content: "\f12e"; }
+
+.fa-money-check::before {
+  content: "\f53c"; }
+
+.fa-star-half-stroke::before {
+  content: "\f5c0"; }
+
+.fa-star-half-alt::before {
+  content: "\f5c0"; }
+
+.fa-code::before {
+  content: "\f121"; }
+
+.fa-whiskey-glass::before {
+  content: "\f7a0"; }
+
+.fa-glass-whiskey::before {
+  content: "\f7a0"; }
+
+.fa-building-circle-exclamation::before {
+  content: "\e4d3"; }
+
+.fa-magnifying-glass-chart::before {
+  content: "\e522"; }
+
+.fa-arrow-up-right-from-square::before {
+  content: "\f08e"; }
+
+.fa-external-link::before {
+  content: "\f08e"; }
+
+.fa-cubes-stacked::before {
+  content: "\e4e6"; }
+
+.fa-won-sign::before {
+  content: "\f159"; }
+
+.fa-krw::before {
+  content: "\f159"; }
+
+.fa-won::before {
+  content: "\f159"; }
+
+.fa-virus-covid::before {
+  content: "\e4a8"; }
+
+.fa-austral-sign::before {
+  content: "\e0a9"; }
+
+.fa-f::before {
+  content: "\46"; }
+
+.fa-leaf::before {
+  content: "\f06c"; }
+
+.fa-road::before {
+  content: "\f018"; }
+
+.fa-taxi::before {
+  content: "\f1ba"; }
+
+.fa-cab::before {
+  content: "\f1ba"; }
+
+.fa-person-circle-plus::before {
+  content: "\e541"; }
+
+.fa-chart-pie::before {
+  content: "\f200"; }
+
+.fa-pie-chart::before {
+  content: "\f200"; }
+
+.fa-bolt-lightning::before {
+  content: "\e0b7"; }
+
+.fa-sack-xmark::before {
+  content: "\e56a"; }
+
+.fa-file-excel::before {
+  content: "\f1c3"; }
+
+.fa-file-contract::before {
+  content: "\f56c"; }
+
+.fa-fish-fins::before {
+  content: "\e4f2"; }
+
+.fa-building-flag::before {
+  content: "\e4d5"; }
+
+.fa-face-grin-beam::before {
+  content: "\f582"; }
+
+.fa-grin-beam::before {
+  content: "\f582"; }
+
+.fa-object-ungroup::before {
+  content: "\f248"; }
+
+.fa-poop::before {
+  content: "\f619"; }
+
+.fa-location-pin::before {
+  content: "\f041"; }
+
+.fa-map-marker::before {
+  content: "\f041"; }
+
+.fa-kaaba::before {
+  content: "\f66b"; }
+
+.fa-toilet-paper::before {
+  content: "\f71e"; }
+
+.fa-helmet-safety::before {
+  content: "\f807"; }
+
+.fa-hard-hat::before {
+  content: "\f807"; }
+
+.fa-hat-hard::before {
+  content: "\f807"; }
+
+.fa-eject::before {
+  content: "\f052"; }
+
+.fa-circle-right::before {
+  content: "\f35a"; }
+
+.fa-arrow-alt-circle-right::before {
+  content: "\f35a"; }
+
+.fa-plane-circle-check::before {
+  content: "\e555"; }
+
+.fa-face-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-meh-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-object-group::before {
+  content: "\f247"; }
+
+.fa-chart-line::before {
+  content: "\f201"; }
+
+.fa-line-chart::before {
+  content: "\f201"; }
+
+.fa-mask-ventilator::before {
+  content: "\e524"; }
+
+.fa-arrow-right::before {
+  content: "\f061"; }
+
+.fa-signs-post::before {
+  content: "\f277"; }
+
+.fa-map-signs::before {
+  content: "\f277"; }
+
+.fa-cash-register::before {
+  content: "\f788"; }
+
+.fa-person-circle-question::before {
+  content: "\e542"; }
+
+.fa-h::before {
+  content: "\48"; }
+
+.fa-tarp::before {
+  content: "\e57b"; }
+
+.fa-screwdriver-wrench::before {
+  content: "\f7d9"; }
+
+.fa-tools::before {
+  content: "\f7d9"; }
+
+.fa-arrows-to-eye::before {
+  content: "\e4bf"; }
+
+.fa-plug-circle-bolt::before {
+  content: "\e55b"; }
+
+.fa-heart::before {
+  content: "\f004"; }
+
+.fa-mars-and-venus::before {
+  content: "\f224"; }
+
+.fa-house-user::before {
+  content: "\e1b0"; }
+
+.fa-home-user::before {
+  content: "\e1b0"; }
+
+.fa-dumpster-fire::before {
+  content: "\f794"; }
+
+.fa-house-crack::before {
+  content: "\e3b1"; }
+
+.fa-martini-glass-citrus::before {
+  content: "\f561"; }
+
+.fa-cocktail::before {
+  content: "\f561"; }
+
+.fa-face-surprise::before {
+  content: "\f5c2"; }
+
+.fa-surprise::before {
+  content: "\f5c2"; }
+
+.fa-bottle-water::before {
+  content: "\e4c5"; }
+
+.fa-circle-pause::before {
+  content: "\f28b"; }
+
+.fa-pause-circle::before {
+  content: "\f28b"; }
+
+.fa-toilet-paper-slash::before {
+  content: "\e072"; }
+
+.fa-apple-whole::before {
+  content: "\f5d1"; }
+
+.fa-apple-alt::before {
+  content: "\f5d1"; }
+
+.fa-kitchen-set::before {
+  content: "\e51a"; }
+
+.fa-r::before {
+  content: "\52"; }
+
+.fa-temperature-quarter::before {
+  content: "\f2ca"; }
+
+.fa-temperature-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-quarter::before {
+  content: "\f2ca"; }
+
+.fa-cube::before {
+  content: "\f1b2"; }
+
+.fa-bitcoin-sign::before {
+  content: "\e0b4"; }
+
+.fa-shield-dog::before {
+  content: "\e573"; }
+
+.fa-solar-panel::before {
+  content: "\f5ba"; }
+
+.fa-lock-open::before {
+  content: "\f3c1"; }
+
+.fa-elevator::before {
+  content: "\e16d"; }
+
+.fa-money-bill-transfer::before {
+  content: "\e528"; }
+
+.fa-money-bill-trend-up::before {
+  content: "\e529"; }
+
+.fa-house-flood-water-circle-arrow-right::before {
+  content: "\e50f"; }
+
+.fa-square-poll-horizontal::before {
+  content: "\f682"; }
+
+.fa-poll-h::before {
+  content: "\f682"; }
+
+.fa-circle::before {
+  content: "\f111"; }
+
+.fa-backward-fast::before {
+  content: "\f049"; }
+
+.fa-fast-backward::before {
+  content: "\f049"; }
+
+.fa-recycle::before {
+  content: "\f1b8"; }
+
+.fa-user-astronaut::before {
+  content: "\f4fb"; }
+
+.fa-plane-slash::before {
+  content: "\e069"; }
+
+.fa-trademark::before {
+  content: "\f25c"; }
+
+.fa-basketball::before {
+  content: "\f434"; }
+
+.fa-basketball-ball::before {
+  content: "\f434"; }
+
+.fa-satellite-dish::before {
+  content: "\f7c0"; }
+
+.fa-circle-up::before {
+  content: "\f35b"; }
+
+.fa-arrow-alt-circle-up::before {
+  content: "\f35b"; }
+
+.fa-mobile-screen-button::before {
+  content: "\f3cd"; }
+
+.fa-mobile-alt::before {
+  content: "\f3cd"; }
+
+.fa-volume-high::before {
+  content: "\f028"; }
+
+.fa-volume-up::before {
+  content: "\f028"; }
+
+.fa-users-rays::before {
+  content: "\e593"; }
+
+.fa-wallet::before {
+  content: "\f555"; }
+
+.fa-clipboard-check::before {
+  content: "\f46c"; }
+
+.fa-file-audio::before {
+  content: "\f1c7"; }
+
+.fa-burger::before {
+  content: "\f805"; }
+
+.fa-hamburger::before {
+  content: "\f805"; }
+
+.fa-wrench::before {
+  content: "\f0ad"; }
+
+.fa-bugs::before {
+  content: "\e4d0"; }
+
+.fa-rupee-sign::before {
+  content: "\f156"; }
+
+.fa-rupee::before {
+  content: "\f156"; }
+
+.fa-file-image::before {
+  content: "\f1c5"; }
+
+.fa-circle-question::before {
+  content: "\f059"; }
+
+.fa-question-circle::before {
+  content: "\f059"; }
+
+.fa-plane-departure::before {
+  content: "\f5b0"; }
+
+.fa-handshake-slash::before {
+  content: "\e060"; }
+
+.fa-book-bookmark::before {
+  content: "\e0bb"; }
+
+.fa-code-branch::before {
+  content: "\f126"; }
+
+.fa-hat-cowboy::before {
+  content: "\f8c0"; }
+
+.fa-bridge::before {
+  content: "\e4c8"; }
+
+.fa-phone-flip::before {
+  content: "\f879"; }
+
+.fa-phone-alt::before {
+  content: "\f879"; }
+
+.fa-truck-front::before {
+  content: "\e2b7"; }
+
+.fa-cat::before {
+  content: "\f6be"; }
+
+.fa-anchor-circle-exclamation::before {
+  content: "\e4ab"; }
+
+.fa-truck-field::before {
+  content: "\e58d"; }
+
+.fa-route::before {
+  content: "\f4d7"; }
+
+.fa-clipboard-question::before {
+  content: "\e4e3"; }
+
+.fa-panorama::before {
+  content: "\e209"; }
+
+.fa-comment-medical::before {
+  content: "\f7f5"; }
+
+.fa-teeth-open::before {
+  content: "\f62f"; }
+
+.fa-file-circle-minus::before {
+  content: "\e4ed"; }
+
+.fa-tags::before {
+  content: "\f02c"; }
+
+.fa-wine-glass::before {
+  content: "\f4e3"; }
+
+.fa-forward-fast::before {
+  content: "\f050"; }
+
+.fa-fast-forward::before {
+  content: "\f050"; }
+
+.fa-face-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-square-parking::before {
+  content: "\f540"; }
+
+.fa-parking::before {
+  content: "\f540"; }
+
+.fa-house-signal::before {
+  content: "\e012"; }
+
+.fa-bars-progress::before {
+  content: "\f828"; }
+
+.fa-tasks-alt::before {
+  content: "\f828"; }
+
+.fa-faucet-drip::before {
+  content: "\e006"; }
+
+.fa-cart-flatbed::before {
+  content: "\f474"; }
+
+.fa-dolly-flatbed::before {
+  content: "\f474"; }
+
+.fa-ban-smoking::before {
+  content: "\f54d"; }
+
+.fa-smoking-ban::before {
+  content: "\f54d"; }
+
+.fa-terminal::before {
+  content: "\f120"; }
+
+.fa-mobile-button::before {
+  content: "\f10b"; }
+
+.fa-house-medical-flag::before {
+  content: "\e514"; }
+
+.fa-basket-shopping::before {
+  content: "\f291"; }
+
+.fa-shopping-basket::before {
+  content: "\f291"; }
+
+.fa-tape::before {
+  content: "\f4db"; }
+
+.fa-bus-simple::before {
+  content: "\f55e"; }
+
+.fa-bus-alt::before {
+  content: "\f55e"; }
+
+.fa-eye::before {
+  content: "\f06e"; }
+
+.fa-face-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-audio-description::before {
+  content: "\f29e"; }
+
+.fa-person-military-to-person::before {
+  content: "\e54c"; }
+
+.fa-file-shield::before {
+  content: "\e4f0"; }
+
+.fa-user-slash::before {
+  content: "\f506"; }
+
+.fa-pen::before {
+  content: "\f304"; }
+
+.fa-tower-observation::before {
+  content: "\e586"; }
+
+.fa-file-code::before {
+  content: "\f1c9"; }
+
+.fa-signal::before {
+  content: "\f012"; }
+
+.fa-signal-5::before {
+  content: "\f012"; }
+
+.fa-signal-perfect::before {
+  content: "\f012"; }
+
+.fa-bus::before {
+  content: "\f207"; }
+
+.fa-heart-circle-xmark::before {
+  content: "\e501"; }
+
+.fa-house-chimney::before {
+  content: "\e3af"; }
+
+.fa-home-lg::before {
+  content: "\e3af"; }
+
+.fa-window-maximize::before {
+  content: "\f2d0"; }
+
+.fa-face-frown::before {
+  content: "\f119"; }
+
+.fa-frown::before {
+  content: "\f119"; }
+
+.fa-prescription::before {
+  content: "\f5b1"; }
+
+.fa-shop::before {
+  content: "\f54f"; }
+
+.fa-store-alt::before {
+  content: "\f54f"; }
+
+.fa-floppy-disk::before {
+  content: "\f0c7"; }
+
+.fa-save::before {
+  content: "\f0c7"; }
+
+.fa-vihara::before {
+  content: "\f6a7"; }
+
+.fa-scale-unbalanced::before {
+  content: "\f515"; }
+
+.fa-balance-scale-left::before {
+  content: "\f515"; }
+
+.fa-sort-up::before {
+  content: "\f0de"; }
+
+.fa-sort-asc::before {
+  content: "\f0de"; }
+
+.fa-comment-dots::before {
+  content: "\f4ad"; }
+
+.fa-commenting::before {
+  content: "\f4ad"; }
+
+.fa-plant-wilt::before {
+  content: "\e5aa"; }
+
+.fa-diamond::before {
+  content: "\f219"; }
+
+.fa-face-grin-squint::before {
+  content: "\f585"; }
+
+.fa-grin-squint::before {
+  content: "\f585"; }
+
+.fa-hand-holding-dollar::before {
+  content: "\f4c0"; }
+
+.fa-hand-holding-usd::before {
+  content: "\f4c0"; }
+
+.fa-bacterium::before {
+  content: "\e05a"; }
+
+.fa-hand-pointer::before {
+  content: "\f25a"; }
+
+.fa-drum-steelpan::before {
+  content: "\f56a"; }
+
+.fa-hand-scissors::before {
+  content: "\f257"; }
+
+.fa-hands-praying::before {
+  content: "\f684"; }
+
+.fa-praying-hands::before {
+  content: "\f684"; }
+
+.fa-arrow-rotate-right::before {
+  content: "\f01e"; }
+
+.fa-arrow-right-rotate::before {
+  content: "\f01e"; }
+
+.fa-arrow-rotate-forward::before {
+  content: "\f01e"; }
+
+.fa-redo::before {
+  content: "\f01e"; }
+
+.fa-biohazard::before {
+  content: "\f780"; }
+
+.fa-location-crosshairs::before {
+  content: "\f601"; }
+
+.fa-location::before {
+  content: "\f601"; }
+
+.fa-mars-double::before {
+  content: "\f227"; }
+
+.fa-child-dress::before {
+  content: "\e59c"; }
+
+.fa-users-between-lines::before {
+  content: "\e591"; }
+
+.fa-lungs-virus::before {
+  content: "\e067"; }
+
+.fa-face-grin-tears::before {
+  content: "\f588"; }
+
+.fa-grin-tears::before {
+  content: "\f588"; }
+
+.fa-phone::before {
+  content: "\f095"; }
+
+.fa-calendar-xmark::before {
+  content: "\f273"; }
+
+.fa-calendar-times::before {
+  content: "\f273"; }
+
+.fa-child-reaching::before {
+  content: "\e59d"; }
+
+.fa-head-side-virus::before {
+  content: "\e064"; }
+
+.fa-user-gear::before {
+  content: "\f4fe"; }
+
+.fa-user-cog::before {
+  content: "\f4fe"; }
+
+.fa-arrow-up-1-9::before {
+  content: "\f163"; }
+
+.fa-sort-numeric-up::before {
+  content: "\f163"; }
+
+.fa-door-closed::before {
+  content: "\f52a"; }
+
+.fa-shield-virus::before {
+  content: "\e06c"; }
+
+.fa-dice-six::before {
+  content: "\f526"; }
+
+.fa-mosquito-net::before {
+  content: "\e52c"; }
+
+.fa-bridge-water::before {
+  content: "\e4ce"; }
+
+.fa-person-booth::before {
+  content: "\f756"; }
+
+.fa-text-width::before {
+  content: "\f035"; }
+
+.fa-hat-wizard::before {
+  content: "\f6e8"; }
+
+.fa-pen-fancy::before {
+  content: "\f5ac"; }
+
+.fa-person-digging::before {
+  content: "\f85e"; }
+
+.fa-digging::before {
+  content: "\f85e"; }
+
+.fa-trash::before {
+  content: "\f1f8"; }
+
+.fa-gauge-simple::before {
+  content: "\f629"; }
+
+.fa-gauge-simple-med::before {
+  content: "\f629"; }
+
+.fa-tachometer-average::before {
+  content: "\f629"; }
+
+.fa-book-medical::before {
+  content: "\f7e6"; }
+
+.fa-poo::before {
+  content: "\f2fe"; }
+
+.fa-quote-right::before {
+  content: "\f10e"; }
+
+.fa-quote-right-alt::before {
+  content: "\f10e"; }
+
+.fa-shirt::before {
+  content: "\f553"; }
+
+.fa-t-shirt::before {
+  content: "\f553"; }
+
+.fa-tshirt::before {
+  content: "\f553"; }
+
+.fa-cubes::before {
+  content: "\f1b3"; }
+
+.fa-divide::before {
+  content: "\f529"; }
+
+.fa-tenge-sign::before {
+  content: "\f7d7"; }
+
+.fa-tenge::before {
+  content: "\f7d7"; }
+
+.fa-headphones::before {
+  content: "\f025"; }
+
+.fa-hands-holding::before {
+  content: "\f4c2"; }
+
+.fa-hands-clapping::before {
+  content: "\e1a8"; }
+
+.fa-republican::before {
+  content: "\f75e"; }
+
+.fa-arrow-left::before {
+  content: "\f060"; }
+
+.fa-person-circle-xmark::before {
+  content: "\e543"; }
+
+.fa-ruler::before {
+  content: "\f545"; }
+
+.fa-align-left::before {
+  content: "\f036"; }
+
+.fa-dice-d6::before {
+  content: "\f6d1"; }
+
+.fa-restroom::before {
+  content: "\f7bd"; }
+
+.fa-j::before {
+  content: "\4a"; }
+
+.fa-users-viewfinder::before {
+  content: "\e595"; }
+
+.fa-file-video::before {
+  content: "\f1c8"; }
+
+.fa-up-right-from-square::before {
+  content: "\f35d"; }
+
+.fa-external-link-alt::before {
+  content: "\f35d"; }
+
+.fa-table-cells::before {
+  content: "\f00a"; }
+
+.fa-th::before {
+  content: "\f00a"; }
+
+.fa-file-pdf::before {
+  content: "\f1c1"; }
+
+.fa-book-bible::before {
+  content: "\f647"; }
+
+.fa-bible::before {
+  content: "\f647"; }
+
+.fa-o::before {
+  content: "\4f"; }
+
+.fa-suitcase-medical::before {
+  content: "\f0fa"; }
+
+.fa-medkit::before {
+  content: "\f0fa"; }
+
+.fa-user-secret::before {
+  content: "\f21b"; }
+
+.fa-otter::before {
+  content: "\f700"; }
+
+.fa-person-dress::before {
+  content: "\f182"; }
+
+.fa-female::before {
+  content: "\f182"; }
+
+.fa-comment-dollar::before {
+  content: "\f651"; }
+
+.fa-business-time::before {
+  content: "\f64a"; }
+
+.fa-briefcase-clock::before {
+  content: "\f64a"; }
+
+.fa-table-cells-large::before {
+  content: "\f009"; }
+
+.fa-th-large::before {
+  content: "\f009"; }
+
+.fa-book-tanakh::before {
+  content: "\f827"; }
+
+.fa-tanakh::before {
+  content: "\f827"; }
+
+.fa-phone-volume::before {
+  content: "\f2a0"; }
+
+.fa-volume-control-phone::before {
+  content: "\f2a0"; }
+
+.fa-hat-cowboy-side::before {
+  content: "\f8c1"; }
+
+.fa-clipboard-user::before {
+  content: "\f7f3"; }
+
+.fa-child::before {
+  content: "\f1ae"; }
+
+.fa-lira-sign::before {
+  content: "\f195"; }
+
+.fa-satellite::before {
+  content: "\f7bf"; }
+
+.fa-plane-lock::before {
+  content: "\e558"; }
+
+.fa-tag::before {
+  content: "\f02b"; }
+
+.fa-comment::before {
+  content: "\f075"; }
+
+.fa-cake-candles::before {
+  content: "\f1fd"; }
+
+.fa-birthday-cake::before {
+  content: "\f1fd"; }
+
+.fa-cake::before {
+  content: "\f1fd"; }
+
+.fa-envelope::before {
+  content: "\f0e0"; }
+
+.fa-angles-up::before {
+  content: "\f102"; }
+
+.fa-angle-double-up::before {
+  content: "\f102"; }
+
+.fa-paperclip::before {
+  content: "\f0c6"; }
+
+.fa-arrow-right-to-city::before {
+  content: "\e4b3"; }
+
+.fa-ribbon::before {
+  content: "\f4d6"; }
+
+.fa-lungs::before {
+  content: "\f604"; }
+
+.fa-arrow-up-9-1::before {
+  content: "\f887"; }
+
+.fa-sort-numeric-up-alt::before {
+  content: "\f887"; }
+
+.fa-litecoin-sign::before {
+  content: "\e1d3"; }
+
+.fa-border-none::before {
+  content: "\f850"; }
+
+.fa-circle-nodes::before {
+  content: "\e4e2"; }
+
+.fa-parachute-box::before {
+  content: "\f4cd"; }
+
+.fa-indent::before {
+  content: "\f03c"; }
+
+.fa-truck-field-un::before {
+  content: "\e58e"; }
+
+.fa-hourglass::before {
+  content: "\f254"; }
+
+.fa-hourglass-empty::before {
+  content: "\f254"; }
+
+.fa-mountain::before {
+  content: "\f6fc"; }
+
+.fa-user-doctor::before {
+  content: "\f0f0"; }
+
+.fa-user-md::before {
+  content: "\f0f0"; }
+
+.fa-circle-info::before {
+  content: "\f05a"; }
+
+.fa-info-circle::before {
+  content: "\f05a"; }
+
+.fa-cloud-meatball::before {
+  content: "\f73b"; }
+
+.fa-camera::before {
+  content: "\f030"; }
+
+.fa-camera-alt::before {
+  content: "\f030"; }
+
+.fa-square-virus::before {
+  content: "\e578"; }
+
+.fa-meteor::before {
+  content: "\f753"; }
+
+.fa-car-on::before {
+  content: "\e4dd"; }
+
+.fa-sleigh::before {
+  content: "\f7cc"; }
+
+.fa-arrow-down-1-9::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-asc::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-down::before {
+  content: "\f162"; }
+
+.fa-hand-holding-droplet::before {
+  content: "\f4c1"; }
+
+.fa-hand-holding-water::before {
+  content: "\f4c1"; }
+
+.fa-water::before {
+  content: "\f773"; }
+
+.fa-calendar-check::before {
+  content: "\f274"; }
+
+.fa-braille::before {
+  content: "\f2a1"; }
+
+.fa-prescription-bottle-medical::before {
+  content: "\f486"; }
+
+.fa-prescription-bottle-alt::before {
+  content: "\f486"; }
+
+.fa-landmark::before {
+  content: "\f66f"; }
+
+.fa-truck::before {
+  content: "\f0d1"; }
+
+.fa-crosshairs::before {
+  content: "\f05b"; }
+
+.fa-person-cane::before {
+  content: "\e53c"; }
+
+.fa-tent::before {
+  content: "\e57d"; }
+
+.fa-vest-patches::before {
+  content: "\e086"; }
+
+.fa-check-double::before {
+  content: "\f560"; }
+
+.fa-arrow-down-a-z::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-asc::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-down::before {
+  content: "\f15d"; }
+
+.fa-money-bill-wheat::before {
+  content: "\e52a"; }
+
+.fa-cookie::before {
+  content: "\f563"; }
+
+.fa-arrow-rotate-left::before {
+  content: "\f0e2"; }
+
+.fa-arrow-left-rotate::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-back::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-backward::before {
+  content: "\f0e2"; }
+
+.fa-undo::before {
+  content: "\f0e2"; }
+
+.fa-hard-drive::before {
+  content: "\f0a0"; }
+
+.fa-hdd::before {
+  content: "\f0a0"; }
+
+.fa-face-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-dumbbell::before {
+  content: "\f44b"; }
+
+.fa-rectangle-list::before {
+  content: "\f022"; }
+
+.fa-list-alt::before {
+  content: "\f022"; }
+
+.fa-tarp-droplet::before {
+  content: "\e57c"; }
+
+.fa-house-medical-circle-check::before {
+  content: "\e511"; }
+
+.fa-person-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-calendar-plus::before {
+  content: "\f271"; }
+
+.fa-plane-arrival::before {
+  content: "\f5af"; }
+
+.fa-circle-left::before {
+  content: "\f359"; }
+
+.fa-arrow-alt-circle-left::before {
+  content: "\f359"; }
+
+.fa-train-subway::before {
+  content: "\f239"; }
+
+.fa-subway::before {
+  content: "\f239"; }
+
+.fa-chart-gantt::before {
+  content: "\e0e4"; }
+
+.fa-indian-rupee-sign::before {
+  content: "\e1bc"; }
+
+.fa-indian-rupee::before {
+  content: "\e1bc"; }
+
+.fa-inr::before {
+  content: "\e1bc"; }
+
+.fa-crop-simple::before {
+  content: "\f565"; }
+
+.fa-crop-alt::before {
+  content: "\f565"; }
+
+.fa-money-bill-1::before {
+  content: "\f3d1"; }
+
+.fa-money-bill-alt::before {
+  content: "\f3d1"; }
+
+.fa-left-long::before {
+  content: "\f30a"; }
+
+.fa-long-arrow-alt-left::before {
+  content: "\f30a"; }
+
+.fa-dna::before {
+  content: "\f471"; }
+
+.fa-virus-slash::before {
+  content: "\e075"; }
+
+.fa-minus::before {
+  content: "\f068"; }
+
+.fa-subtract::before {
+  content: "\f068"; }
+
+.fa-chess::before {
+  content: "\f439"; }
+
+.fa-arrow-left-long::before {
+  content: "\f177"; }
+
+.fa-long-arrow-left::before {
+  content: "\f177"; }
+
+.fa-plug-circle-check::before {
+  content: "\e55c"; }
+
+.fa-street-view::before {
+  content: "\f21d"; }
+
+.fa-franc-sign::before {
+  content: "\e18f"; }
+
+.fa-volume-off::before {
+  content: "\f026"; }
+
+.fa-hands-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-hands-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-gear::before {
+  content: "\f013"; }
+
+.fa-cog::before {
+  content: "\f013"; }
+
+.fa-droplet-slash::before {
+  content: "\f5c7"; }
+
+.fa-tint-slash::before {
+  content: "\f5c7"; }
+
+.fa-mosque::before {
+  content: "\f678"; }
+
+.fa-mosquito::before {
+  content: "\e52b"; }
+
+.fa-star-of-david::before {
+  content: "\f69a"; }
+
+.fa-person-military-rifle::before {
+  content: "\e54b"; }
+
+.fa-cart-shopping::before {
+  content: "\f07a"; }
+
+.fa-shopping-cart::before {
+  content: "\f07a"; }
+
+.fa-vials::before {
+  content: "\f493"; }
+
+.fa-plug-circle-plus::before {
+  content: "\e55f"; }
+
+.fa-place-of-worship::before {
+  content: "\f67f"; }
+
+.fa-grip-vertical::before {
+  content: "\f58e"; }
+
+.fa-arrow-turn-up::before {
+  content: "\f148"; }
+
+.fa-level-up::before {
+  content: "\f148"; }
+
+.fa-u::before {
+  content: "\55"; }
+
+.fa-square-root-variable::before {
+  content: "\f698"; }
+
+.fa-square-root-alt::before {
+  content: "\f698"; }
+
+.fa-clock::before {
+  content: "\f017"; }
+
+.fa-clock-four::before {
+  content: "\f017"; }
+
+.fa-backward-step::before {
+  content: "\f048"; }
+
+.fa-step-backward::before {
+  content: "\f048"; }
+
+.fa-pallet::before {
+  content: "\f482"; }
+
+.fa-faucet::before {
+  content: "\e005"; }
+
+.fa-baseball-bat-ball::before {
+  content: "\f432"; }
+
+.fa-s::before {
+  content: "\53"; }
+
+.fa-timeline::before {
+  content: "\e29c"; }
+
+.fa-keyboard::before {
+  content: "\f11c"; }
+
+.fa-caret-down::before {
+  content: "\f0d7"; }
+
+.fa-house-chimney-medical::before {
+  content: "\f7f2"; }
+
+.fa-clinic-medical::before {
+  content: "\f7f2"; }
+
+.fa-temperature-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-temperature-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-mobile-screen::before {
+  content: "\f3cf"; }
+
+.fa-mobile-android-alt::before {
+  content: "\f3cf"; }
+
+.fa-plane-up::before {
+  content: "\e22d"; }
+
+.fa-piggy-bank::before {
+  content: "\f4d3"; }
+
+.fa-battery-half::before {
+  content: "\f242"; }
+
+.fa-battery-3::before {
+  content: "\f242"; }
+
+.fa-mountain-city::before {
+  content: "\e52e"; }
+
+.fa-coins::before {
+  content: "\f51e"; }
+
+.fa-khanda::before {
+  content: "\f66d"; }
+
+.fa-sliders::before {
+  content: "\f1de"; }
+
+.fa-sliders-h::before {
+  content: "\f1de"; }
+
+.fa-folder-tree::before {
+  content: "\f802"; }
+
+.fa-network-wired::before {
+  content: "\f6ff"; }
+
+.fa-map-pin::before {
+  content: "\f276"; }
+
+.fa-hamsa::before {
+  content: "\f665"; }
+
+.fa-cent-sign::before {
+  content: "\e3f5"; }
+
+.fa-flask::before {
+  content: "\f0c3"; }
+
+.fa-person-pregnant::before {
+  content: "\e31e"; }
+
+.fa-wand-sparkles::before {
+  content: "\f72b"; }
+
+.fa-ellipsis-vertical::before {
+  content: "\f142"; }
+
+.fa-ellipsis-v::before {
+  content: "\f142"; }
+
+.fa-ticket::before {
+  content: "\f145"; }
+
+.fa-power-off::before {
+  content: "\f011"; }
+
+.fa-right-long::before {
+  content: "\f30b"; }
+
+.fa-long-arrow-alt-right::before {
+  content: "\f30b"; }
+
+.fa-flag-usa::before {
+  content: "\f74d"; }
+
+.fa-laptop-file::before {
+  content: "\e51d"; }
+
+.fa-tty::before {
+  content: "\f1e4"; }
+
+.fa-teletype::before {
+  content: "\f1e4"; }
+
+.fa-diagram-next::before {
+  content: "\e476"; }
+
+.fa-person-rifle::before {
+  content: "\e54e"; }
+
+.fa-house-medical-circle-exclamation::before {
+  content: "\e512"; }
+
+.fa-closed-captioning::before {
+  content: "\f20a"; }
+
+.fa-person-hiking::before {
+  content: "\f6ec"; }
+
+.fa-hiking::before {
+  content: "\f6ec"; }
+
+.fa-venus-double::before {
+  content: "\f226"; }
+
+.fa-images::before {
+  content: "\f302"; }
+
+.fa-calculator::before {
+  content: "\f1ec"; }
+
+.fa-people-pulling::before {
+  content: "\e535"; }
+
+.fa-n::before {
+  content: "\4e"; }
+
+.fa-cable-car::before {
+  content: "\f7da"; }
+
+.fa-tram::before {
+  content: "\f7da"; }
+
+.fa-cloud-rain::before {
+  content: "\f73d"; }
+
+.fa-building-circle-xmark::before {
+  content: "\e4d4"; }
+
+.fa-ship::before {
+  content: "\f21a"; }
+
+.fa-arrows-down-to-line::before {
+  content: "\e4b8"; }
+
+.fa-download::before {
+  content: "\f019"; }
+
+.fa-face-grin::before {
+  content: "\f580"; }
+
+.fa-grin::before {
+  content: "\f580"; }
+
+.fa-delete-left::before {
+  content: "\f55a"; }
+
+.fa-backspace::before {
+  content: "\f55a"; }
+
+.fa-eye-dropper::before {
+  content: "\f1fb"; }
+
+.fa-eye-dropper-empty::before {
+  content: "\f1fb"; }
+
+.fa-eyedropper::before {
+  content: "\f1fb"; }
+
+.fa-file-circle-check::before {
+  content: "\e5a0"; }
+
+.fa-forward::before {
+  content: "\f04e"; }
+
+.fa-mobile::before {
+  content: "\f3ce"; }
+
+.fa-mobile-android::before {
+  content: "\f3ce"; }
+
+.fa-mobile-phone::before {
+  content: "\f3ce"; }
+
+.fa-face-meh::before {
+  content: "\f11a"; }
+
+.fa-meh::before {
+  content: "\f11a"; }
+
+.fa-align-center::before {
+  content: "\f037"; }
+
+.fa-book-skull::before {
+  content: "\f6b7"; }
+
+.fa-book-dead::before {
+  content: "\f6b7"; }
+
+.fa-id-card::before {
+  content: "\f2c2"; }
+
+.fa-drivers-license::before {
+  content: "\f2c2"; }
+
+.fa-outdent::before {
+  content: "\f03b"; }
+
+.fa-dedent::before {
+  content: "\f03b"; }
+
+.fa-heart-circle-exclamation::before {
+  content: "\e4fe"; }
+
+.fa-house::before {
+  content: "\f015"; }
+
+.fa-home::before {
+  content: "\f015"; }
+
+.fa-home-alt::before {
+  content: "\f015"; }
+
+.fa-home-lg-alt::before {
+  content: "\f015"; }
+
+.fa-calendar-week::before {
+  content: "\f784"; }
+
+.fa-laptop-medical::before {
+  content: "\f812"; }
+
+.fa-b::before {
+  content: "\42"; }
+
+.fa-file-medical::before {
+  content: "\f477"; }
+
+.fa-dice-one::before {
+  content: "\f525"; }
+
+.fa-kiwi-bird::before {
+  content: "\f535"; }
+
+.fa-arrow-right-arrow-left::before {
+  content: "\f0ec"; }
+
+.fa-exchange::before {
+  content: "\f0ec"; }
+
+.fa-rotate-right::before {
+  content: "\f2f9"; }
+
+.fa-redo-alt::before {
+  content: "\f2f9"; }
+
+.fa-rotate-forward::before {
+  content: "\f2f9"; }
+
+.fa-utensils::before {
+  content: "\f2e7"; }
+
+.fa-cutlery::before {
+  content: "\f2e7"; }
+
+.fa-arrow-up-wide-short::before {
+  content: "\f161"; }
+
+.fa-sort-amount-up::before {
+  content: "\f161"; }
+
+.fa-mill-sign::before {
+  content: "\e1ed"; }
+
+.fa-bowl-rice::before {
+  content: "\e2eb"; }
+
+.fa-skull::before {
+  content: "\f54c"; }
+
+.fa-tower-broadcast::before {
+  content: "\f519"; }
+
+.fa-broadcast-tower::before {
+  content: "\f519"; }
+
+.fa-truck-pickup::before {
+  content: "\f63c"; }
+
+.fa-up-long::before {
+  content: "\f30c"; }
+
+.fa-long-arrow-alt-up::before {
+  content: "\f30c"; }
+
+.fa-stop::before {
+  content: "\f04d"; }
+
+.fa-code-merge::before {
+  content: "\f387"; }
+
+.fa-upload::before {
+  content: "\f093"; }
+
+.fa-hurricane::before {
+  content: "\f751"; }
+
+.fa-mound::before {
+  content: "\e52d"; }
+
+.fa-toilet-portable::before {
+  content: "\e583"; }
+
+.fa-compact-disc::before {
+  content: "\f51f"; }
+
+.fa-file-arrow-down::before {
+  content: "\f56d"; }
+
+.fa-file-download::before {
+  content: "\f56d"; }
+
+.fa-caravan::before {
+  content: "\f8ff"; }
+
+.fa-shield-cat::before {
+  content: "\e572"; }
+
+.fa-bolt::before {
+  content: "\f0e7"; }
+
+.fa-zap::before {
+  content: "\f0e7"; }
+
+.fa-glass-water::before {
+  content: "\e4f4"; }
+
+.fa-oil-well::before {
+  content: "\e532"; }
+
+.fa-vault::before {
+  content: "\e2c5"; }
+
+.fa-mars::before {
+  content: "\f222"; }
+
+.fa-toilet::before {
+  content: "\f7d8"; }
+
+.fa-plane-circle-xmark::before {
+  content: "\e557"; }
+
+.fa-yen-sign::before {
+  content: "\f157"; }
+
+.fa-cny::before {
+  content: "\f157"; }
+
+.fa-jpy::before {
+  content: "\f157"; }
+
+.fa-rmb::before {
+  content: "\f157"; }
+
+.fa-yen::before {
+  content: "\f157"; }
+
+.fa-ruble-sign::before {
+  content: "\f158"; }
+
+.fa-rouble::before {
+  content: "\f158"; }
+
+.fa-rub::before {
+  content: "\f158"; }
+
+.fa-ruble::before {
+  content: "\f158"; }
+
+.fa-sun::before {
+  content: "\f185"; }
+
+.fa-guitar::before {
+  content: "\f7a6"; }
+
+.fa-face-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-horse-head::before {
+  content: "\f7ab"; }
+
+.fa-bore-hole::before {
+  content: "\e4c3"; }
+
+.fa-industry::before {
+  content: "\f275"; }
+
+.fa-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrow-alt-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrows-turn-to-dots::before {
+  content: "\e4c1"; }
+
+.fa-florin-sign::before {
+  content: "\e184"; }
+
+.fa-arrow-down-short-wide::before {
+  content: "\f884"; }
+
+.fa-sort-amount-desc::before {
+  content: "\f884"; }
+
+.fa-sort-amount-down-alt::before {
+  content: "\f884"; }
+
+.fa-less-than::before {
+  content: "\3c"; }
+
+.fa-angle-down::before {
+  content: "\f107"; }
+
+.fa-car-tunnel::before {
+  content: "\e4de"; }
+
+.fa-head-side-cough::before {
+  content: "\e061"; }
+
+.fa-grip-lines::before {
+  content: "\f7a4"; }
+
+.fa-thumbs-down::before {
+  content: "\f165"; }
+
+.fa-user-lock::before {
+  content: "\f502"; }
+
+.fa-arrow-right-long::before {
+  content: "\f178"; }
+
+.fa-long-arrow-right::before {
+  content: "\f178"; }
+
+.fa-anchor-circle-xmark::before {
+  content: "\e4ac"; }
+
+.fa-ellipsis::before {
+  content: "\f141"; }
+
+.fa-ellipsis-h::before {
+  content: "\f141"; }
+
+.fa-chess-pawn::before {
+  content: "\f443"; }
+
+.fa-kit-medical::before {
+  content: "\f479"; }
+
+.fa-first-aid::before {
+  content: "\f479"; }
+
+.fa-person-through-window::before {
+  content: "\e5a9"; }
+
+.fa-toolbox::before {
+  content: "\f552"; }
+
+.fa-hands-holding-circle::before {
+  content: "\e4fb"; }
+
+.fa-bug::before {
+  content: "\f188"; }
+
+.fa-credit-card::before {
+  content: "\f09d"; }
+
+.fa-credit-card-alt::before {
+  content: "\f09d"; }
+
+.fa-car::before {
+  content: "\f1b9"; }
+
+.fa-automobile::before {
+  content: "\f1b9"; }
+
+.fa-hand-holding-hand::before {
+  content: "\e4f7"; }
+
+.fa-book-open-reader::before {
+  content: "\f5da"; }
+
+.fa-book-reader::before {
+  content: "\f5da"; }
+
+.fa-mountain-sun::before {
+  content: "\e52f"; }
+
+.fa-arrows-left-right-to-line::before {
+  content: "\e4ba"; }
+
+.fa-dice-d20::before {
+  content: "\f6cf"; }
+
+.fa-truck-droplet::before {
+  content: "\e58c"; }
+
+.fa-file-circle-xmark::before {
+  content: "\e5a1"; }
+
+.fa-temperature-arrow-up::before {
+  content: "\e040"; }
+
+.fa-temperature-up::before {
+  content: "\e040"; }
+
+.fa-medal::before {
+  content: "\f5a2"; }
+
+.fa-bed::before {
+  content: "\f236"; }
+
+.fa-square-h::before {
+  content: "\f0fd"; }
+
+.fa-h-square::before {
+  content: "\f0fd"; }
+
+.fa-podcast::before {
+  content: "\f2ce"; }
+
+.fa-temperature-full::before {
+  content: "\f2c7"; }
+
+.fa-temperature-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-full::before {
+  content: "\f2c7"; }
+
+.fa-bell::before {
+  content: "\f0f3"; }
+
+.fa-superscript::before {
+  content: "\f12b"; }
+
+.fa-plug-circle-xmark::before {
+  content: "\e560"; }
+
+.fa-star-of-life::before {
+  content: "\f621"; }
+
+.fa-phone-slash::before {
+  content: "\f3dd"; }
+
+.fa-paint-roller::before {
+  content: "\f5aa"; }
+
+.fa-handshake-angle::before {
+  content: "\f4c4"; }
+
+.fa-hands-helping::before {
+  content: "\f4c4"; }
+
+.fa-location-dot::before {
+  content: "\f3c5"; }
+
+.fa-map-marker-alt::before {
+  content: "\f3c5"; }
+
+.fa-file::before {
+  content: "\f15b"; }
+
+.fa-greater-than::before {
+  content: "\3e"; }
+
+.fa-person-swimming::before {
+  content: "\f5c4"; }
+
+.fa-swimmer::before {
+  content: "\f5c4"; }
+
+.fa-arrow-down::before {
+  content: "\f063"; }
+
+.fa-droplet::before {
+  content: "\f043"; }
+
+.fa-tint::before {
+  content: "\f043"; }
+
+.fa-eraser::before {
+  content: "\f12d"; }
+
+.fa-earth-americas::before {
+  content: "\f57d"; }
+
+.fa-earth::before {
+  content: "\f57d"; }
+
+.fa-earth-america::before {
+  content: "\f57d"; }
+
+.fa-globe-americas::before {
+  content: "\f57d"; }
+
+.fa-person-burst::before {
+  content: "\e53b"; }
+
+.fa-dove::before {
+  content: "\f4ba"; }
+
+.fa-battery-empty::before {
+  content: "\f244"; }
+
+.fa-battery-0::before {
+  content: "\f244"; }
+
+.fa-socks::before {
+  content: "\f696"; }
+
+.fa-inbox::before {
+  content: "\f01c"; }
+
+.fa-section::before {
+  content: "\e447"; }
+
+.fa-gauge-high::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt-fast::before {
+  content: "\f625"; }
+
+.fa-envelope-open-text::before {
+  content: "\f658"; }
+
+.fa-hospital::before {
+  content: "\f0f8"; }
+
+.fa-hospital-alt::before {
+  content: "\f0f8"; }
+
+.fa-hospital-wide::before {
+  content: "\f0f8"; }
+
+.fa-wine-bottle::before {
+  content: "\f72f"; }
+
+.fa-chess-rook::before {
+  content: "\f447"; }
+
+.fa-bars-staggered::before {
+  content: "\f550"; }
+
+.fa-reorder::before {
+  content: "\f550"; }
+
+.fa-stream::before {
+  content: "\f550"; }
+
+.fa-dharmachakra::before {
+  content: "\f655"; }
+
+.fa-hotdog::before {
+  content: "\f80f"; }
+
+.fa-person-walking-with-cane::before {
+  content: "\f29d"; }
+
+.fa-blind::before {
+  content: "\f29d"; }
+
+.fa-drum::before {
+  content: "\f569"; }
+
+.fa-ice-cream::before {
+  content: "\f810"; }
+
+.fa-heart-circle-bolt::before {
+  content: "\e4fc"; }
+
+.fa-fax::before {
+  content: "\f1ac"; }
+
+.fa-paragraph::before {
+  content: "\f1dd"; }
+
+.fa-check-to-slot::before {
+  content: "\f772"; }
+
+.fa-vote-yea::before {
+  content: "\f772"; }
+
+.fa-star-half::before {
+  content: "\f089"; }
+
+.fa-boxes-stacked::before {
+  content: "\f468"; }
+
+.fa-boxes::before {
+  content: "\f468"; }
+
+.fa-boxes-alt::before {
+  content: "\f468"; }
+
+.fa-link::before {
+  content: "\f0c1"; }
+
+.fa-chain::before {
+  content: "\f0c1"; }
+
+.fa-ear-listen::before {
+  content: "\f2a2"; }
+
+.fa-assistive-listening-systems::before {
+  content: "\f2a2"; }
+
+.fa-tree-city::before {
+  content: "\e587"; }
+
+.fa-play::before {
+  content: "\f04b"; }
+
+.fa-font::before {
+  content: "\f031"; }
+
+.fa-rupiah-sign::before {
+  content: "\e23d"; }
+
+.fa-magnifying-glass::before {
+  content: "\f002"; }
+
+.fa-search::before {
+  content: "\f002"; }
+
+.fa-table-tennis-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-ping-pong-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-table-tennis::before {
+  content: "\f45d"; }
+
+.fa-person-dots-from-line::before {
+  content: "\f470"; }
+
+.fa-diagnoses::before {
+  content: "\f470"; }
+
+.fa-trash-can-arrow-up::before {
+  content: "\f82a"; }
+
+.fa-trash-restore-alt::before {
+  content: "\f82a"; }
+
+.fa-naira-sign::before {
+  content: "\e1f6"; }
+
+.fa-cart-arrow-down::before {
+  content: "\f218"; }
+
+.fa-walkie-talkie::before {
+  content: "\f8ef"; }
+
+.fa-file-pen::before {
+  content: "\f31c"; }
+
+.fa-file-edit::before {
+  content: "\f31c"; }
+
+.fa-receipt::before {
+  content: "\f543"; }
+
+.fa-square-pen::before {
+  content: "\f14b"; }
+
+.fa-pen-square::before {
+  content: "\f14b"; }
+
+.fa-pencil-square::before {
+  content: "\f14b"; }
+
+.fa-suitcase-rolling::before {
+  content: "\f5c1"; }
+
+.fa-person-circle-exclamation::before {
+  content: "\e53f"; }
+
+.fa-chevron-down::before {
+  content: "\f078"; }
+
+.fa-battery-full::before {
+  content: "\f240"; }
+
+.fa-battery::before {
+  content: "\f240"; }
+
+.fa-battery-5::before {
+  content: "\f240"; }
+
+.fa-skull-crossbones::before {
+  content: "\f714"; }
+
+.fa-code-compare::before {
+  content: "\e13a"; }
+
+.fa-list-ul::before {
+  content: "\f0ca"; }
+
+.fa-list-dots::before {
+  content: "\f0ca"; }
+
+.fa-school-lock::before {
+  content: "\e56f"; }
+
+.fa-tower-cell::before {
+  content: "\e585"; }
+
+.fa-down-long::before {
+  content: "\f309"; }
+
+.fa-long-arrow-alt-down::before {
+  content: "\f309"; }
+
+.fa-ranking-star::before {
+  content: "\e561"; }
+
+.fa-chess-king::before {
+  content: "\f43f"; }
+
+.fa-person-harassing::before {
+  content: "\e549"; }
+
+.fa-brazilian-real-sign::before {
+  content: "\e46c"; }
+
+.fa-landmark-dome::before {
+  content: "\f752"; }
+
+.fa-landmark-alt::before {
+  content: "\f752"; }
+
+.fa-arrow-up::before {
+  content: "\f062"; }
+
+.fa-tv::before {
+  content: "\f26c"; }
+
+.fa-television::before {
+  content: "\f26c"; }
+
+.fa-tv-alt::before {
+  content: "\f26c"; }
+
+.fa-shrimp::before {
+  content: "\e448"; }
+
+.fa-list-check::before {
+  content: "\f0ae"; }
+
+.fa-tasks::before {
+  content: "\f0ae"; }
+
+.fa-jug-detergent::before {
+  content: "\e519"; }
+
+.fa-circle-user::before {
+  content: "\f2bd"; }
+
+.fa-user-circle::before {
+  content: "\f2bd"; }
+
+.fa-user-shield::before {
+  content: "\f505"; }
+
+.fa-wind::before {
+  content: "\f72e"; }
+
+.fa-car-burst::before {
+  content: "\f5e1"; }
+
+.fa-car-crash::before {
+  content: "\f5e1"; }
+
+.fa-y::before {
+  content: "\59"; }
+
+.fa-person-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-truck-fast::before {
+  content: "\f48b"; }
+
+.fa-shipping-fast::before {
+  content: "\f48b"; }
+
+.fa-fish::before {
+  content: "\f578"; }
+
+.fa-user-graduate::before {
+  content: "\f501"; }
+
+.fa-circle-half-stroke::before {
+  content: "\f042"; }
+
+.fa-adjust::before {
+  content: "\f042"; }
+
+.fa-clapperboard::before {
+  content: "\e131"; }
+
+.fa-circle-radiation::before {
+  content: "\f7ba"; }
+
+.fa-radiation-alt::before {
+  content: "\f7ba"; }
+
+.fa-baseball::before {
+  content: "\f433"; }
+
+.fa-baseball-ball::before {
+  content: "\f433"; }
+
+.fa-jet-fighter-up::before {
+  content: "\e518"; }
+
+.fa-diagram-project::before {
+  content: "\f542"; }
+
+.fa-project-diagram::before {
+  content: "\f542"; }
+
+.fa-copy::before {
+  content: "\f0c5"; }
+
+.fa-volume-xmark::before {
+  content: "\f6a9"; }
+
+.fa-volume-mute::before {
+  content: "\f6a9"; }
+
+.fa-volume-times::before {
+  content: "\f6a9"; }
+
+.fa-hand-sparkles::before {
+  content: "\e05d"; }
+
+.fa-grip::before {
+  content: "\f58d"; }
+
+.fa-grip-horizontal::before {
+  content: "\f58d"; }
+
+.fa-share-from-square::before {
+  content: "\f14d"; }
+
+.fa-share-square::before {
+  content: "\f14d"; }
+
+.fa-child-combatant::before {
+  content: "\e4e0"; }
+
+.fa-child-rifle::before {
+  content: "\e4e0"; }
+
+.fa-gun::before {
+  content: "\e19b"; }
+
+.fa-square-phone::before {
+  content: "\f098"; }
+
+.fa-phone-square::before {
+  content: "\f098"; }
+
+.fa-plus::before {
+  content: "\2b"; }
+
+.fa-add::before {
+  content: "\2b"; }
+
+.fa-expand::before {
+  content: "\f065"; }
+
+.fa-computer::before {
+  content: "\e4e5"; }
+
+.fa-xmark::before {
+  content: "\f00d"; }
+
+.fa-close::before {
+  content: "\f00d"; }
+
+.fa-multiply::before {
+  content: "\f00d"; }
+
+.fa-remove::before {
+  content: "\f00d"; }
+
+.fa-times::before {
+  content: "\f00d"; }
+
+.fa-arrows-up-down-left-right::before {
+  content: "\f047"; }
+
+.fa-arrows::before {
+  content: "\f047"; }
+
+.fa-chalkboard-user::before {
+  content: "\f51c"; }
+
+.fa-chalkboard-teacher::before {
+  content: "\f51c"; }
+
+.fa-peso-sign::before {
+  content: "\e222"; }
+
+.fa-building-shield::before {
+  content: "\e4d8"; }
+
+.fa-baby::before {
+  content: "\f77c"; }
+
+.fa-users-line::before {
+  content: "\e592"; }
+
+.fa-quote-left::before {
+  content: "\f10d"; }
+
+.fa-quote-left-alt::before {
+  content: "\f10d"; }
+
+.fa-tractor::before {
+  content: "\f722"; }
+
+.fa-trash-arrow-up::before {
+  content: "\f829"; }
+
+.fa-trash-restore::before {
+  content: "\f829"; }
+
+.fa-arrow-down-up-lock::before {
+  content: "\e4b0"; }
+
+.fa-lines-leaning::before {
+  content: "\e51e"; }
+
+.fa-ruler-combined::before {
+  content: "\f546"; }
+
+.fa-copyright::before {
+  content: "\f1f9"; }
+
+.fa-equals::before {
+  content: "\3d"; }
+
+.fa-blender::before {
+  content: "\f517"; }
+
+.fa-teeth::before {
+  content: "\f62e"; }
+
+.fa-shekel-sign::before {
+  content: "\f20b"; }
+
+.fa-ils::before {
+  content: "\f20b"; }
+
+.fa-shekel::before {
+  content: "\f20b"; }
+
+.fa-sheqel::before {
+  content: "\f20b"; }
+
+.fa-sheqel-sign::before {
+  content: "\f20b"; }
+
+.fa-map::before {
+  content: "\f279"; }
+
+.fa-rocket::before {
+  content: "\f135"; }
+
+.fa-photo-film::before {
+  content: "\f87c"; }
+
+.fa-photo-video::before {
+  content: "\f87c"; }
+
+.fa-folder-minus::before {
+  content: "\f65d"; }
+
+.fa-store::before {
+  content: "\f54e"; }
+
+.fa-arrow-trend-up::before {
+  content: "\e098"; }
+
+.fa-plug-circle-minus::before {
+  content: "\e55e"; }
+
+.fa-sign-hanging::before {
+  content: "\f4d9"; }
+
+.fa-sign::before {
+  content: "\f4d9"; }
+
+.fa-bezier-curve::before {
+  content: "\f55b"; }
+
+.fa-bell-slash::before {
+  content: "\f1f6"; }
+
+.fa-tablet::before {
+  content: "\f3fb"; }
+
+.fa-tablet-android::before {
+  content: "\f3fb"; }
+
+.fa-school-flag::before {
+  content: "\e56e"; }
+
+.fa-fill::before {
+  content: "\f575"; }
+
+.fa-angle-up::before {
+  content: "\f106"; }
+
+.fa-drumstick-bite::before {
+  content: "\f6d7"; }
+
+.fa-holly-berry::before {
+  content: "\f7aa"; }
+
+.fa-chevron-left::before {
+  content: "\f053"; }
+
+.fa-bacteria::before {
+  content: "\e059"; }
+
+.fa-hand-lizard::before {
+  content: "\f258"; }
+
+.fa-notdef::before {
+  content: "\e1fe"; }
+
+.fa-disease::before {
+  content: "\f7fa"; }
+
+.fa-briefcase-medical::before {
+  content: "\f469"; }
+
+.fa-genderless::before {
+  content: "\f22d"; }
+
+.fa-chevron-right::before {
+  content: "\f054"; }
+
+.fa-retweet::before {
+  content: "\f079"; }
+
+.fa-car-rear::before {
+  content: "\f5de"; }
+
+.fa-car-alt::before {
+  content: "\f5de"; }
+
+.fa-pump-soap::before {
+  content: "\e06b"; }
+
+.fa-video-slash::before {
+  content: "\f4e2"; }
+
+.fa-battery-quarter::before {
+  content: "\f243"; }
+
+.fa-battery-2::before {
+  content: "\f243"; }
+
+.fa-radio::before {
+  content: "\f8d7"; }
+
+.fa-baby-carriage::before {
+  content: "\f77d"; }
+
+.fa-carriage-baby::before {
+  content: "\f77d"; }
+
+.fa-traffic-light::before {
+  content: "\f637"; }
+
+.fa-thermometer::before {
+  content: "\f491"; }
+
+.fa-vr-cardboard::before {
+  content: "\f729"; }
+
+.fa-hand-middle-finger::before {
+  content: "\f806"; }
+
+.fa-percent::before {
+  content: "\25"; }
+
+.fa-percentage::before {
+  content: "\25"; }
+
+.fa-truck-moving::before {
+  content: "\f4df"; }
+
+.fa-glass-water-droplet::before {
+  content: "\e4f5"; }
+
+.fa-display::before {
+  content: "\e163"; }
+
+.fa-face-smile::before {
+  content: "\f118"; }
+
+.fa-smile::before {
+  content: "\f118"; }
+
+.fa-thumbtack::before {
+  content: "\f08d"; }
+
+.fa-thumb-tack::before {
+  content: "\f08d"; }
+
+.fa-trophy::before {
+  content: "\f091"; }
+
+.fa-person-praying::before {
+  content: "\f683"; }
+
+.fa-pray::before {
+  content: "\f683"; }
+
+.fa-hammer::before {
+  content: "\f6e3"; }
+
+.fa-hand-peace::before {
+  content: "\f25b"; }
+
+.fa-rotate::before {
+  content: "\f2f1"; }
+
+.fa-sync-alt::before {
+  content: "\f2f1"; }
+
+.fa-spinner::before {
+  content: "\f110"; }
+
+.fa-robot::before {
+  content: "\f544"; }
+
+.fa-peace::before {
+  content: "\f67c"; }
+
+.fa-gears::before {
+  content: "\f085"; }
+
+.fa-cogs::before {
+  content: "\f085"; }
+
+.fa-warehouse::before {
+  content: "\f494"; }
+
+.fa-arrow-up-right-dots::before {
+  content: "\e4b7"; }
+
+.fa-splotch::before {
+  content: "\f5bc"; }
+
+.fa-face-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-dice-four::before {
+  content: "\f524"; }
+
+.fa-sim-card::before {
+  content: "\f7c4"; }
+
+.fa-transgender::before {
+  content: "\f225"; }
+
+.fa-transgender-alt::before {
+  content: "\f225"; }
+
+.fa-mercury::before {
+  content: "\f223"; }
+
+.fa-arrow-turn-down::before {
+  content: "\f149"; }
+
+.fa-level-down::before {
+  content: "\f149"; }
+
+.fa-person-falling-burst::before {
+  content: "\e547"; }
+
+.fa-award::before {
+  content: "\f559"; }
+
+.fa-ticket-simple::before {
+  content: "\f3ff"; }
+
+.fa-ticket-alt::before {
+  content: "\f3ff"; }
+
+.fa-building::before {
+  content: "\f1ad"; }
+
+.fa-angles-left::before {
+  content: "\f100"; }
+
+.fa-angle-double-left::before {
+  content: "\f100"; }
+
+.fa-qrcode::before {
+  content: "\f029"; }
+
+.fa-clock-rotate-left::before {
+  content: "\f1da"; }
+
+.fa-history::before {
+  content: "\f1da"; }
+
+.fa-face-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-file-export::before {
+  content: "\f56e"; }
+
+.fa-arrow-right-from-file::before {
+  content: "\f56e"; }
+
+.fa-shield::before {
+  content: "\f132"; }
+
+.fa-shield-blank::before {
+  content: "\f132"; }
+
+.fa-arrow-up-short-wide::before {
+  content: "\f885"; }
+
+.fa-sort-amount-up-alt::before {
+  content: "\f885"; }
+
+.fa-house-medical::before {
+  content: "\e3b2"; }
+
+.fa-golf-ball-tee::before {
+  content: "\f450"; }
+
+.fa-golf-ball::before {
+  content: "\f450"; }
+
+.fa-circle-chevron-left::before {
+  content: "\f137"; }
+
+.fa-chevron-circle-left::before {
+  content: "\f137"; }
+
+.fa-house-chimney-window::before {
+  content: "\e00d"; }
+
+.fa-pen-nib::before {
+  content: "\f5ad"; }
+
+.fa-tent-arrow-turn-left::before {
+  content: "\e580"; }
+
+.fa-tents::before {
+  content: "\e582"; }
+
+.fa-wand-magic::before {
+  content: "\f0d0"; }
+
+.fa-magic::before {
+  content: "\f0d0"; }
+
+.fa-dog::before {
+  content: "\f6d3"; }
+
+.fa-carrot::before {
+  content: "\f787"; }
+
+.fa-moon::before {
+  content: "\f186"; }
+
+.fa-wine-glass-empty::before {
+  content: "\f5ce"; }
+
+.fa-wine-glass-alt::before {
+  content: "\f5ce"; }
+
+.fa-cheese::before {
+  content: "\f7ef"; }
+
+.fa-yin-yang::before {
+  content: "\f6ad"; }
+
+.fa-music::before {
+  content: "\f001"; }
+
+.fa-code-commit::before {
+  content: "\f386"; }
+
+.fa-temperature-low::before {
+  content: "\f76b"; }
+
+.fa-person-biking::before {
+  content: "\f84a"; }
+
+.fa-biking::before {
+  content: "\f84a"; }
+
+.fa-broom::before {
+  content: "\f51a"; }
+
+.fa-shield-heart::before {
+  content: "\e574"; }
+
+.fa-gopuram::before {
+  content: "\f664"; }
+
+.fa-earth-oceania::before {
+  content: "\e47b"; }
+
+.fa-globe-oceania::before {
+  content: "\e47b"; }
+
+.fa-square-xmark::before {
+  content: "\f2d3"; }
+
+.fa-times-square::before {
+  content: "\f2d3"; }
+
+.fa-xmark-square::before {
+  content: "\f2d3"; }
+
+.fa-hashtag::before {
+  content: "\23"; }
+
+.fa-up-right-and-down-left-from-center::before {
+  content: "\f424"; }
+
+.fa-expand-alt::before {
+  content: "\f424"; }
+
+.fa-oil-can::before {
+  content: "\f613"; }
+
+.fa-t::before {
+  content: "\54"; }
+
+.fa-hippo::before {
+  content: "\f6ed"; }
+
+.fa-chart-column::before {
+  content: "\e0e3"; }
+
+.fa-infinity::before {
+  content: "\f534"; }
+
+.fa-vial-circle-check::before {
+  content: "\e596"; }
+
+.fa-person-arrow-down-to-line::before {
+  content: "\e538"; }
+
+.fa-voicemail::before {
+  content: "\f897"; }
+
+.fa-fan::before {
+  content: "\f863"; }
+
+.fa-person-walking-luggage::before {
+  content: "\e554"; }
+
+.fa-up-down::before {
+  content: "\f338"; }
+
+.fa-arrows-alt-v::before {
+  content: "\f338"; }
+
+.fa-cloud-moon-rain::before {
+  content: "\f73c"; }
+
+.fa-calendar::before {
+  content: "\f133"; }
+
+.fa-trailer::before {
+  content: "\e041"; }
+
+.fa-bahai::before {
+  content: "\f666"; }
+
+.fa-haykal::before {
+  content: "\f666"; }
+
+.fa-sd-card::before {
+  content: "\f7c2"; }
+
+.fa-dragon::before {
+  content: "\f6d5"; }
+
+.fa-shoe-prints::before {
+  content: "\f54b"; }
+
+.fa-circle-plus::before {
+  content: "\f055"; }
+
+.fa-plus-circle::before {
+  content: "\f055"; }
+
+.fa-face-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-hand-holding::before {
+  content: "\f4bd"; }
+
+.fa-plug-circle-exclamation::before {
+  content: "\e55d"; }
+
+.fa-link-slash::before {
+  content: "\f127"; }
+
+.fa-chain-broken::before {
+  content: "\f127"; }
+
+.fa-chain-slash::before {
+  content: "\f127"; }
+
+.fa-unlink::before {
+  content: "\f127"; }
+
+.fa-clone::before {
+  content: "\f24d"; }
+
+.fa-person-walking-arrow-loop-left::before {
+  content: "\e551"; }
+
+.fa-arrow-up-z-a::before {
+  content: "\f882"; }
+
+.fa-sort-alpha-up-alt::before {
+  content: "\f882"; }
+
+.fa-fire-flame-curved::before {
+  content: "\f7e4"; }
+
+.fa-fire-alt::before {
+  content: "\f7e4"; }
+
+.fa-tornado::before {
+  content: "\f76f"; }
+
+.fa-file-circle-plus::before {
+  content: "\e494"; }
+
+.fa-book-quran::before {
+  content: "\f687"; }
+
+.fa-quran::before {
+  content: "\f687"; }
+
+.fa-anchor::before {
+  content: "\f13d"; }
+
+.fa-border-all::before {
+  content: "\f84c"; }
+
+.fa-face-angry::before {
+  content: "\f556"; }
+
+.fa-angry::before {
+  content: "\f556"; }
+
+.fa-cookie-bite::before {
+  content: "\f564"; }
+
+.fa-arrow-trend-down::before {
+  content: "\e097"; }
+
+.fa-rss::before {
+  content: "\f09e"; }
+
+.fa-feed::before {
+  content: "\f09e"; }
+
+.fa-draw-polygon::before {
+  content: "\f5ee"; }
+
+.fa-scale-balanced::before {
+  content: "\f24e"; }
+
+.fa-balance-scale::before {
+  content: "\f24e"; }
+
+.fa-gauge-simple-high::before {
+  content: "\f62a"; }
+
+.fa-tachometer::before {
+  content: "\f62a"; }
+
+.fa-tachometer-fast::before {
+  content: "\f62a"; }
+
+.fa-shower::before {
+  content: "\f2cc"; }
+
+.fa-desktop::before {
+  content: "\f390"; }
+
+.fa-desktop-alt::before {
+  content: "\f390"; }
+
+.fa-m::before {
+  content: "\4d"; }
+
+.fa-table-list::before {
+  content: "\f00b"; }
+
+.fa-th-list::before {
+  content: "\f00b"; }
+
+.fa-comment-sms::before {
+  content: "\f7cd"; }
+
+.fa-sms::before {
+  content: "\f7cd"; }
+
+.fa-book::before {
+  content: "\f02d"; }
+
+.fa-user-plus::before {
+  content: "\f234"; }
+
+.fa-check::before {
+  content: "\f00c"; }
+
+.fa-battery-three-quarters::before {
+  content: "\f241"; }
+
+.fa-battery-4::before {
+  content: "\f241"; }
+
+.fa-house-circle-check::before {
+  content: "\e509"; }
+
+.fa-angle-left::before {
+  content: "\f104"; }
+
+.fa-diagram-successor::before {
+  content: "\e47a"; }
+
+.fa-truck-arrow-right::before {
+  content: "\e58b"; }
+
+.fa-arrows-split-up-and-left::before {
+  content: "\e4bc"; }
+
+.fa-hand-fist::before {
+  content: "\f6de"; }
+
+.fa-fist-raised::before {
+  content: "\f6de"; }
+
+.fa-cloud-moon::before {
+  content: "\f6c3"; }
+
+.fa-briefcase::before {
+  content: "\f0b1"; }
+
+.fa-person-falling::before {
+  content: "\e546"; }
+
+.fa-image-portrait::before {
+  content: "\f3e0"; }
+
+.fa-portrait::before {
+  content: "\f3e0"; }
+
+.fa-user-tag::before {
+  content: "\f507"; }
+
+.fa-rug::before {
+  content: "\e569"; }
+
+.fa-earth-europe::before {
+  content: "\f7a2"; }
+
+.fa-globe-europe::before {
+  content: "\f7a2"; }
+
+.fa-cart-flatbed-suitcase::before {
+  content: "\f59d"; }
+
+.fa-luggage-cart::before {
+  content: "\f59d"; }
+
+.fa-rectangle-xmark::before {
+  content: "\f410"; }
+
+.fa-rectangle-times::before {
+  content: "\f410"; }
+
+.fa-times-rectangle::before {
+  content: "\f410"; }
+
+.fa-window-close::before {
+  content: "\f410"; }
+
+.fa-baht-sign::before {
+  content: "\e0ac"; }
+
+.fa-book-open::before {
+  content: "\f518"; }
+
+.fa-book-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-handcuffs::before {
+  content: "\e4f8"; }
+
+.fa-triangle-exclamation::before {
+  content: "\f071"; }
+
+.fa-exclamation-triangle::before {
+  content: "\f071"; }
+
+.fa-warning::before {
+  content: "\f071"; }
+
+.fa-database::before {
+  content: "\f1c0"; }
+
+.fa-share::before {
+  content: "\f064"; }
+
+.fa-arrow-turn-right::before {
+  content: "\f064"; }
+
+.fa-mail-forward::before {
+  content: "\f064"; }
+
+.fa-bottle-droplet::before {
+  content: "\e4c4"; }
+
+.fa-mask-face::before {
+  content: "\e1d7"; }
+
+.fa-hill-rockslide::before {
+  content: "\e508"; }
+
+.fa-right-left::before {
+  content: "\f362"; }
+
+.fa-exchange-alt::before {
+  content: "\f362"; }
+
+.fa-paper-plane::before {
+  content: "\f1d8"; }
+
+.fa-road-circle-exclamation::before {
+  content: "\e565"; }
+
+.fa-dungeon::before {
+  content: "\f6d9"; }
+
+.fa-align-right::before {
+  content: "\f038"; }
+
+.fa-money-bill-1-wave::before {
+  content: "\f53b"; }
+
+.fa-money-bill-wave-alt::before {
+  content: "\f53b"; }
+
+.fa-life-ring::before {
+  content: "\f1cd"; }
+
+.fa-hands::before {
+  content: "\f2a7"; }
+
+.fa-sign-language::before {
+  content: "\f2a7"; }
+
+.fa-signing::before {
+  content: "\f2a7"; }
+
+.fa-calendar-day::before {
+  content: "\f783"; }
+
+.fa-water-ladder::before {
+  content: "\f5c5"; }
+
+.fa-ladder-water::before {
+  content: "\f5c5"; }
+
+.fa-swimming-pool::before {
+  content: "\f5c5"; }
+
+.fa-arrows-up-down::before {
+  content: "\f07d"; }
+
+.fa-arrows-v::before {
+  content: "\f07d"; }
+
+.fa-face-grimace::before {
+  content: "\f57f"; }
+
+.fa-grimace::before {
+  content: "\f57f"; }
+
+.fa-wheelchair-move::before {
+  content: "\e2ce"; }
+
+.fa-wheelchair-alt::before {
+  content: "\e2ce"; }
+
+.fa-turn-down::before {
+  content: "\f3be"; }
+
+.fa-level-down-alt::before {
+  content: "\f3be"; }
+
+.fa-person-walking-arrow-right::before {
+  content: "\e552"; }
+
+.fa-square-envelope::before {
+  content: "\f199"; }
+
+.fa-envelope-square::before {
+  content: "\f199"; }
+
+.fa-dice::before {
+  content: "\f522"; }
+
+.fa-bowling-ball::before {
+  content: "\f436"; }
+
+.fa-brain::before {
+  content: "\f5dc"; }
+
+.fa-bandage::before {
+  content: "\f462"; }
+
+.fa-band-aid::before {
+  content: "\f462"; }
+
+.fa-calendar-minus::before {
+  content: "\f272"; }
+
+.fa-circle-xmark::before {
+  content: "\f057"; }
+
+.fa-times-circle::before {
+  content: "\f057"; }
+
+.fa-xmark-circle::before {
+  content: "\f057"; }
+
+.fa-gifts::before {
+  content: "\f79c"; }
+
+.fa-hotel::before {
+  content: "\f594"; }
+
+.fa-earth-asia::before {
+  content: "\f57e"; }
+
+.fa-globe-asia::before {
+  content: "\f57e"; }
+
+.fa-id-card-clip::before {
+  content: "\f47f"; }
+
+.fa-id-card-alt::before {
+  content: "\f47f"; }
+
+.fa-magnifying-glass-plus::before {
+  content: "\f00e"; }
+
+.fa-search-plus::before {
+  content: "\f00e"; }
+
+.fa-thumbs-up::before {
+  content: "\f164"; }
+
+.fa-user-clock::before {
+  content: "\f4fd"; }
+
+.fa-hand-dots::before {
+  content: "\f461"; }
+
+.fa-allergies::before {
+  content: "\f461"; }
+
+.fa-file-invoice::before {
+  content: "\f570"; }
+
+.fa-window-minimize::before {
+  content: "\f2d1"; }
+
+.fa-mug-saucer::before {
+  content: "\f0f4"; }
+
+.fa-coffee::before {
+  content: "\f0f4"; }
+
+.fa-brush::before {
+  content: "\f55d"; }
+
+.fa-mask::before {
+  content: "\f6fa"; }
+
+.fa-magnifying-glass-minus::before {
+  content: "\f010"; }
+
+.fa-search-minus::before {
+  content: "\f010"; }
+
+.fa-ruler-vertical::before {
+  content: "\f548"; }
+
+.fa-user-large::before {
+  content: "\f406"; }
+
+.fa-user-alt::before {
+  content: "\f406"; }
+
+.fa-train-tram::before {
+  content: "\e5b4"; }
+
+.fa-user-nurse::before {
+  content: "\f82f"; }
+
+.fa-syringe::before {
+  content: "\f48e"; }
+
+.fa-cloud-sun::before {
+  content: "\f6c4"; }
+
+.fa-stopwatch-20::before {
+  content: "\e06f"; }
+
+.fa-square-full::before {
+  content: "\f45c"; }
+
+.fa-magnet::before {
+  content: "\f076"; }
+
+.fa-jar::before {
+  content: "\e516"; }
+
+.fa-note-sticky::before {
+  content: "\f249"; }
+
+.fa-sticky-note::before {
+  content: "\f249"; }
+
+.fa-bug-slash::before {
+  content: "\e490"; }
+
+.fa-arrow-up-from-water-pump::before {
+  content: "\e4b6"; }
+
+.fa-bone::before {
+  content: "\f5d7"; }
+
+.fa-user-injured::before {
+  content: "\f728"; }
+
+.fa-face-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-plane::before {
+  content: "\f072"; }
+
+.fa-tent-arrows-down::before {
+  content: "\e581"; }
+
+.fa-exclamation::before {
+  content: "\21"; }
+
+.fa-arrows-spin::before {
+  content: "\e4bb"; }
+
+.fa-print::before {
+  content: "\f02f"; }
+
+.fa-turkish-lira-sign::before {
+  content: "\e2bb"; }
+
+.fa-try::before {
+  content: "\e2bb"; }
+
+.fa-turkish-lira::before {
+  content: "\e2bb"; }
+
+.fa-dollar-sign::before {
+  content: "\24"; }
+
+.fa-dollar::before {
+  content: "\24"; }
+
+.fa-usd::before {
+  content: "\24"; }
+
+.fa-x::before {
+  content: "\58"; }
+
+.fa-magnifying-glass-dollar::before {
+  content: "\f688"; }
+
+.fa-search-dollar::before {
+  content: "\f688"; }
+
+.fa-users-gear::before {
+  content: "\f509"; }
+
+.fa-users-cog::before {
+  content: "\f509"; }
+
+.fa-person-military-pointing::before {
+  content: "\e54a"; }
+
+.fa-building-columns::before {
+  content: "\f19c"; }
+
+.fa-bank::before {
+  content: "\f19c"; }
+
+.fa-institution::before {
+  content: "\f19c"; }
+
+.fa-museum::before {
+  content: "\f19c"; }
+
+.fa-university::before {
+  content: "\f19c"; }
+
+.fa-umbrella::before {
+  content: "\f0e9"; }
+
+.fa-trowel::before {
+  content: "\e589"; }
+
+.fa-d::before {
+  content: "\44"; }
+
+.fa-stapler::before {
+  content: "\e5af"; }
+
+.fa-masks-theater::before {
+  content: "\f630"; }
+
+.fa-theater-masks::before {
+  content: "\f630"; }
+
+.fa-kip-sign::before {
+  content: "\e1c4"; }
+
+.fa-hand-point-left::before {
+  content: "\f0a5"; }
+
+.fa-handshake-simple::before {
+  content: "\f4c6"; }
+
+.fa-handshake-alt::before {
+  content: "\f4c6"; }
+
+.fa-jet-fighter::before {
+  content: "\f0fb"; }
+
+.fa-fighter-jet::before {
+  content: "\f0fb"; }
+
+.fa-square-share-nodes::before {
+  content: "\f1e1"; }
+
+.fa-share-alt-square::before {
+  content: "\f1e1"; }
+
+.fa-barcode::before {
+  content: "\f02a"; }
+
+.fa-plus-minus::before {
+  content: "\e43c"; }
+
+.fa-video::before {
+  content: "\f03d"; }
+
+.fa-video-camera::before {
+  content: "\f03d"; }
+
+.fa-graduation-cap::before {
+  content: "\f19d"; }
+
+.fa-mortar-board::before {
+  content: "\f19d"; }
+
+.fa-hand-holding-medical::before {
+  content: "\e05c"; }
+
+.fa-person-circle-check::before {
+  content: "\e53e"; }
+
+.fa-turn-up::before {
+  content: "\f3bf"; }
+
+.fa-level-up-alt::before {
+  content: "\f3bf"; }
+
+.sr-only,
+.fa-sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+
+.sr-only-focusable:not(:focus),
+.fa-sr-only-focusable:not(:focus) {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+:root, :host {
+  --fa-style-family-brands: 'Font Awesome 6 Brands';
+  --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Brands';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+.fab,
+.fa-brands {
+  font-weight: 400; }
+
+.fa-monero:before {
+  content: "\f3d0"; }
+
+.fa-hooli:before {
+  content: "\f427"; }
+
+.fa-yelp:before {
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+.fa-pied-piper-square:before {
+  content: "\e01e"; }
+
+.fa-creative-commons-nd:before {
+  content: "\f4eb"; }
+
+.fa-palfed:before {
+  content: "\f3d8"; }
+
+.fa-superpowers:before {
+  content: "\f2dd"; }
+
+.fa-resolving:before {
+  content: "\f3e7"; }
+
+.fa-xbox:before {
+  content: "\f412"; }
+
+.fa-searchengin:before {
+  content: "\f3eb"; }
+
+.fa-tiktok:before {
+  content: "\e07b"; }
+
+.fa-square-facebook:before {
+  content: "\f082"; }
+
+.fa-facebook-square:before {
+  content: "\f082"; }
+
+.fa-renren:before {
+  content: "\f18b"; }
+
+.fa-linux:before {
+  content: "\f17c"; }
+
+.fa-glide:before {
+  content: "\f2a5"; }
+
+.fa-linkedin:before {
+  content: "\f08c"; }
+
+.fa-hubspot:before {
+  content: "\f3b2"; }
+
+.fa-deploydog:before {
+  content: "\f38e"; }
+
+.fa-twitch:before {
+  content: "\f1e8"; }
+
+.fa-ravelry:before {
+  content: "\f2d9"; }
+
+.fa-mixer:before {
+  content: "\e056"; }
+
+.fa-square-lastfm:before {
+  content: "\f203"; }
+
+.fa-lastfm-square:before {
+  content: "\f203"; }
+
+.fa-vimeo:before {
+  content: "\f40a"; }
+
+.fa-mendeley:before {
+  content: "\f7b3"; }
+
+.fa-uniregistry:before {
+  content: "\f404"; }
+
+.fa-figma:before {
+  content: "\f799"; }
+
+.fa-creative-commons-remix:before {
+  content: "\f4ee"; }
+
+.fa-cc-amazon-pay:before {
+  content: "\f42d"; }
+
+.fa-dropbox:before {
+  content: "\f16b"; }
+
+.fa-instagram:before {
+  content: "\f16d"; }
+
+.fa-cmplid:before {
+  content: "\e360"; }
+
+.fa-facebook:before {
+  content: "\f09a"; }
+
+.fa-gripfire:before {
+  content: "\f3ac"; }
+
+.fa-jedi-order:before {
+  content: "\f50e"; }
+
+.fa-uikit:before {
+  content: "\f403"; }
+
+.fa-fort-awesome-alt:before {
+  content: "\f3a3"; }
+
+.fa-phabricator:before {
+  content: "\f3db"; }
+
+.fa-ussunnah:before {
+  content: "\f407"; }
+
+.fa-earlybirds:before {
+  content: "\f39a"; }
+
+.fa-trade-federation:before {
+  content: "\f513"; }
+
+.fa-autoprefixer:before {
+  content: "\f41c"; }
+
+.fa-whatsapp:before {
+  content: "\f232"; }
+
+.fa-slideshare:before {
+  content: "\f1e7"; }
+
+.fa-google-play:before {
+  content: "\f3ab"; }
+
+.fa-viadeo:before {
+  content: "\f2a9"; }
+
+.fa-line:before {
+  content: "\f3c0"; }
+
+.fa-google-drive:before {
+  content: "\f3aa"; }
+
+.fa-servicestack:before {
+  content: "\f3ec"; }
+
+.fa-simplybuilt:before {
+  content: "\f215"; }
+
+.fa-bitbucket:before {
+  content: "\f171"; }
+
+.fa-imdb:before {
+  content: "\f2d8"; }
+
+.fa-deezer:before {
+  content: "\e077"; }
+
+.fa-raspberry-pi:before {
+  content: "\f7bb"; }
+
+.fa-jira:before {
+  content: "\f7b1"; }
+
+.fa-docker:before {
+  content: "\f395"; }
+
+.fa-screenpal:before {
+  content: "\e570"; }
+
+.fa-bluetooth:before {
+  content: "\f293"; }
+
+.fa-gitter:before {
+  content: "\f426"; }
+
+.fa-d-and-d:before {
+  content: "\f38d"; }
+
+.fa-microblog:before {
+  content: "\e01a"; }
+
+.fa-cc-diners-club:before {
+  content: "\f24c"; }
+
+.fa-gg-circle:before {
+  content: "\f261"; }
+
+.fa-pied-piper-hat:before {
+  content: "\f4e5"; }
+
+.fa-kickstarter-k:before {
+  content: "\f3bc"; }
+
+.fa-yandex:before {
+  content: "\f413"; }
+
+.fa-readme:before {
+  content: "\f4d5"; }
+
+.fa-html5:before {
+  content: "\f13b"; }
+
+.fa-sellsy:before {
+  content: "\f213"; }
+
+.fa-sass:before {
+  content: "\f41e"; }
+
+.fa-wirsindhandwerk:before {
+  content: "\e2d0"; }
+
+.fa-wsh:before {
+  content: "\e2d0"; }
+
+.fa-buromobelexperte:before {
+  content: "\f37f"; }
+
+.fa-salesforce:before {
+  content: "\f83b"; }
+
+.fa-octopus-deploy:before {
+  content: "\e082"; }
+
+.fa-medapps:before {
+  content: "\f3c6"; }
+
+.fa-ns8:before {
+  content: "\f3d5"; }
+
+.fa-pinterest-p:before {
+  content: "\f231"; }
+
+.fa-apper:before {
+  content: "\f371"; }
+
+.fa-fort-awesome:before {
+  content: "\f286"; }
+
+.fa-waze:before {
+  content: "\f83f"; }
+
+.fa-cc-jcb:before {
+  content: "\f24b"; }
+
+.fa-snapchat:before {
+  content: "\f2ab"; }
+
+.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa-fantasy-flight-games:before {
+  content: "\f6dc"; }
+
+.fa-rust:before {
+  content: "\e07a"; }
+
+.fa-wix:before {
+  content: "\f5cf"; }
+
+.fa-square-behance:before {
+  content: "\f1b5"; }
+
+.fa-behance-square:before {
+  content: "\f1b5"; }
+
+.fa-supple:before {
+  content: "\f3f9"; }
+
+.fa-rebel:before {
+  content: "\f1d0"; }
+
+.fa-css3:before {
+  content: "\f13c"; }
+
+.fa-staylinked:before {
+  content: "\f3f5"; }
+
+.fa-kaggle:before {
+  content: "\f5fa"; }
+
+.fa-space-awesome:before {
+  content: "\e5ac"; }
+
+.fa-deviantart:before {
+  content: "\f1bd"; }
+
+.fa-cpanel:before {
+  content: "\f388"; }
+
+.fa-goodreads-g:before {
+  content: "\f3a9"; }
+
+.fa-square-git:before {
+  content: "\f1d2"; }
+
+.fa-git-square:before {
+  content: "\f1d2"; }
+
+.fa-square-tumblr:before {
+  content: "\f174"; }
+
+.fa-tumblr-square:before {
+  content: "\f174"; }
+
+.fa-trello:before {
+  content: "\f181"; }
+
+.fa-creative-commons-nc-jp:before {
+  content: "\f4ea"; }
+
+.fa-get-pocket:before {
+  content: "\f265"; }
+
+.fa-perbyte:before {
+  content: "\e083"; }
+
+.fa-grunt:before {
+  content: "\f3ad"; }
+
+.fa-weebly:before {
+  content: "\f5cc"; }
+
+.fa-connectdevelop:before {
+  content: "\f20e"; }
+
+.fa-leanpub:before {
+  content: "\f212"; }
+
+.fa-black-tie:before {
+  content: "\f27e"; }
+
+.fa-themeco:before {
+  content: "\f5c6"; }
+
+.fa-python:before {
+  content: "\f3e2"; }
+
+.fa-android:before {
+  content: "\f17b"; }
+
+.fa-bots:before {
+  content: "\e340"; }
+
+.fa-free-code-camp:before {
+  content: "\f2c5"; }
+
+.fa-hornbill:before {
+  content: "\f592"; }
+
+.fa-js:before {
+  content: "\f3b8"; }
+
+.fa-ideal:before {
+  content: "\e013"; }
+
+.fa-git:before {
+  content: "\f1d3"; }
+
+.fa-dev:before {
+  content: "\f6cc"; }
+
+.fa-sketch:before {
+  content: "\f7c6"; }
+
+.fa-yandex-international:before {
+  content: "\f414"; }
+
+.fa-cc-amex:before {
+  content: "\f1f3"; }
+
+.fa-uber:before {
+  content: "\f402"; }
+
+.fa-github:before {
+  content: "\f09b"; }
+
+.fa-php:before {
+  content: "\f457"; }
+
+.fa-alipay:before {
+  content: "\f642"; }
+
+.fa-youtube:before {
+  content: "\f167"; }
+
+.fa-skyatlas:before {
+  content: "\f216"; }
+
+.fa-firefox-browser:before {
+  content: "\e007"; }
+
+.fa-replyd:before {
+  content: "\f3e6"; }
+
+.fa-suse:before {
+  content: "\f7d6"; }
+
+.fa-jenkins:before {
+  content: "\f3b6"; }
+
+.fa-twitter:before {
+  content: "\f099"; }
+
+.fa-rockrms:before {
+  content: "\f3e9"; }
+
+.fa-pinterest:before {
+  content: "\f0d2"; }
+
+.fa-buffer:before {
+  content: "\f837"; }
+
+.fa-npm:before {
+  content: "\f3d4"; }
+
+.fa-yammer:before {
+  content: "\f840"; }
+
+.fa-btc:before {
+  content: "\f15a"; }
+
+.fa-dribbble:before {
+  content: "\f17d"; }
+
+.fa-stumbleupon-circle:before {
+  content: "\f1a3"; }
+
+.fa-internet-explorer:before {
+  content: "\f26b"; }
+
+.fa-stubber:before {
+  content: "\e5c7"; }
+
+.fa-telegram:before {
+  content: "\f2c6"; }
+
+.fa-telegram-plane:before {
+  content: "\f2c6"; }
+
+.fa-old-republic:before {
+  content: "\f510"; }
+
+.fa-odysee:before {
+  content: "\e5c6"; }
+
+.fa-square-whatsapp:before {
+  content: "\f40c"; }
+
+.fa-whatsapp-square:before {
+  content: "\f40c"; }
+
+.fa-node-js:before {
+  content: "\f3d3"; }
+
+.fa-edge-legacy:before {
+  content: "\e078"; }
+
+.fa-slack:before {
+  content: "\f198"; }
+
+.fa-slack-hash:before {
+  content: "\f198"; }
+
+.fa-medrt:before {
+  content: "\f3c8"; }
+
+.fa-usb:before {
+  content: "\f287"; }
+
+.fa-tumblr:before {
+  content: "\f173"; }
+
+.fa-vaadin:before {
+  content: "\f408"; }
+
+.fa-quora:before {
+  content: "\f2c4"; }
+
+.fa-square-x-twitter:before {
+  content: "\e61a"; }
+
+.fa-reacteurope:before {
+  content: "\f75d"; }
+
+.fa-medium:before {
+  content: "\f23a"; }
+
+.fa-medium-m:before {
+  content: "\f23a"; }
+
+.fa-amilia:before {
+  content: "\f36d"; }
+
+.fa-mixcloud:before {
+  content: "\f289"; }
+
+.fa-flipboard:before {
+  content: "\f44d"; }
+
+.fa-viacoin:before {
+  content: "\f237"; }
+
+.fa-critical-role:before {
+  content: "\f6c9"; }
+
+.fa-sitrox:before {
+  content: "\e44a"; }
+
+.fa-discourse:before {
+  content: "\f393"; }
+
+.fa-joomla:before {
+  content: "\f1aa"; }
+
+.fa-mastodon:before {
+  content: "\f4f6"; }
+
+.fa-airbnb:before {
+  content: "\f834"; }
+
+.fa-wolf-pack-battalion:before {
+  content: "\f514"; }
+
+.fa-buy-n-large:before {
+  content: "\f8a6"; }
+
+.fa-gulp:before {
+  content: "\f3ae"; }
+
+.fa-creative-commons-sampling-plus:before {
+  content: "\f4f1"; }
+
+.fa-strava:before {
+  content: "\f428"; }
+
+.fa-ember:before {
+  content: "\f423"; }
+
+.fa-canadian-maple-leaf:before {
+  content: "\f785"; }
+
+.fa-teamspeak:before {
+  content: "\f4f9"; }
+
+.fa-pushed:before {
+  content: "\f3e1"; }
+
+.fa-wordpress-simple:before {
+  content: "\f411"; }
+
+.fa-nutritionix:before {
+  content: "\f3d6"; }
+
+.fa-wodu:before {
+  content: "\e088"; }
+
+.fa-google-pay:before {
+  content: "\e079"; }
+
+.fa-intercom:before {
+  content: "\f7af"; }
+
+.fa-zhihu:before {
+  content: "\f63f"; }
+
+.fa-korvue:before {
+  content: "\f42f"; }
+
+.fa-pix:before {
+  content: "\e43a"; }
+
+.fa-steam-symbol:before {
+  content: "\f3f6"; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+.far,
+.fa-regular {
+  font-weight: 400; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 900;
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+.fas,
+.fa-solid {
+  font-weight: 900; }
+@font-face {
+  font-family: 'Font Awesome 5 Brands';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 900;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
diff --git a/deps/font-awesome-6.4.2/css/all.min.css b/deps/font-awesome-6.4.2/css/all.min.css
new file mode 100644
index 0000000..6604a06
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/all.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 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diff --git a/deps/font-awesome-6.4.2/css/v4-shims.css b/deps/font-awesome-6.4.2/css/v4-shims.css
new file mode 100644
index 0000000..a85953d
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/v4-shims.css
@@ -0,0 +1,2194 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa.fa-glass:before {
+  content: "\f000"; }
+
+.fa.fa-envelope-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-o:before {
+  content: "\f0e0"; }
+
+.fa.fa-star-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-o:before {
+  content: "\f005"; }
+
+.fa.fa-remove:before {
+  content: "\f00d"; }
+
+.fa.fa-close:before {
+  content: "\f00d"; }
+
+.fa.fa-gear:before {
+  content: "\f013"; }
+
+.fa.fa-trash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-trash-o:before {
+  content: "\f2ed"; }
+
+.fa.fa-home:before {
+  content: "\f015"; }
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+.fa.fa-file-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-file-o:before {
+  content: "\f15b"; }
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+.fa.fa-clock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-clock-o:before {
+  content: "\f017"; }
+
+.fa.fa-arrow-circle-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-arrow-circle-o-down:before {
+  content: "\f358"; }
+
+.fa.fa-arrow-circle-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-up:before {
+  content: "\f35b"; }
+
+.fa.fa-play-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-play-circle-o:before {
+  content: "\f144"; }
+
+.fa.fa-repeat:before {
+  content: "\f01e"; }
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+.fa.fa-rotate-right:before {
+  content: "\f01e"; }
+
+.fa.fa-refresh:before {
+  content: "\f021"; }
+
+.fa.fa-list-alt {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-list-alt:before {
+  content: "\f022"; }
+
+.fa.fa-dedent:before {
+  content: "\f03b"; }
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+.fa.fa-video-camera:before {
+  content: "\f03d"; }
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+.fa.fa-picture-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f03e"; }
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+.fa.fa-photo {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-photo:before {
+  content: "\f03e"; }
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+.fa.fa-image {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-image:before {
+  content: "\f03e"; }
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+.fa.fa-map-marker:before {
+  content: "\f3c5"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-pencil-square-o:before {
+  content: "\f044"; }
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+.fa.fa-edit {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-edit:before {
+  content: "\f044"; }
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+.fa.fa-share-square-o:before {
+  content: "\f14d"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-square-o:before {
+  content: "\f14a"; }
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+.fa.fa-arrows:before {
+  content: "\f0b2"; }
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+.fa.fa-times-circle-o {
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+  font-weight: 400; }
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+.fa.fa-times-circle-o:before {
+  content: "\f057"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-circle-o:before {
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+.fa.fa-compress:before {
+  content: "\f422"; }
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+.fa.fa-eye {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-eye-slash {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-warning:before {
+  content: "\f071"; }
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+.fa.fa-calendar:before {
+  content: "\f073"; }
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+  content: "\f338"; }
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+  content: "\f337"; }
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+.fa.fa-bar-chart:before {
+  content: "\e0e3"; }
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+.fa.fa-bar-chart-o:before {
+  content: "\e0e3"; }
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+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
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+  font-weight: 400; }
+
+.fa.fa-pause-circle-o:before {
+  content: "\f28b"; }
+
+.fa.fa-stop-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-stop-circle-o:before {
+  content: "\f28d"; }
+
+.fa.fa-bluetooth {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bluetooth-b {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gitlab {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpbeginner {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpforms {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-envira {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt:before {
+  content: "\f368"; }
+
+.fa.fa-question-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-question-circle-o:before {
+  content: "\f059"; }
+
+.fa.fa-volume-control-phone:before {
+  content: "\f2a0"; }
+
+.fa.fa-asl-interpreting:before {
+  content: "\f2a3"; }
+
+.fa.fa-deafness:before {
+  content: "\f2a4"; }
+
+.fa.fa-hard-of-hearing:before {
+  content: "\f2a4"; }
+
+.fa.fa-glide {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-glide-g {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-signing:before {
+  content: "\f2a7"; }
+
+.fa.fa-viadeo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa.fa-snapchat {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa.fa-snapchat-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa.fa-pied-piper {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-first-order {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yoast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-themeisle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official:before {
+  content: "\f2b3"; }
+
+.fa.fa-google-plus-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-circle:before {
+  content: "\f2b3"; }
+
+.fa.fa-font-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa:before {
+  content: "\f2b4"; }
+
+.fa.fa-handshake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-handshake-o:before {
+  content: "\f2b5"; }
+
+.fa.fa-envelope-open-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-open-o:before {
+  content: "\f2b6"; }
+
+.fa.fa-linode {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-address-book-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-book-o:before {
+  content: "\f2b9"; }
+
+.fa.fa-vcard:before {
+  content: "\f2bb"; }
+
+.fa.fa-address-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-card-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-vcard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-vcard-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-user-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-circle-o:before {
+  content: "\f2bd"; }
+
+.fa.fa-user-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-o:before {
+  content: "\f007"; }
+
+.fa.fa-id-badge {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license:before {
+  content: "\f2c2"; }
+
+.fa.fa-id-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-id-card-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-drivers-license-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-quora {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-free-code-camp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-telegram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-thermometer-4:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer-3:before {
+  content: "\f2c8"; }
+
+.fa.fa-thermometer-2:before {
+  content: "\f2c9"; }
+
+.fa.fa-thermometer-1:before {
+  content: "\f2ca"; }
+
+.fa.fa-thermometer-0:before {
+  content: "\f2cb"; }
+
+.fa.fa-bathtub:before {
+  content: "\f2cd"; }
+
+.fa.fa-s15:before {
+  content: "\f2cd"; }
+
+.fa.fa-window-maximize {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-restore {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle:before {
+  content: "\f410"; }
+
+.fa.fa-window-close-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-close-o:before {
+  content: "\f410"; }
+
+.fa.fa-times-rectangle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle-o:before {
+  content: "\f410"; }
+
+.fa.fa-bandcamp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-grav {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-etsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-imdb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ravelry {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast:before {
+  content: "\f2da"; }
+
+.fa.fa-snowflake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-snowflake-o:before {
+  content: "\f2dc"; }
+
+.fa.fa-superpowers {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpexplorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-meetup {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
diff --git a/deps/font-awesome-6.4.2/css/v4-shims.min.css b/deps/font-awesome-6.4.2/css/v4-shims.min.css
new file mode 100644
index 0000000..64e4e8d
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/v4-shims.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 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diff --git a/deps/font-awesome-6.4.2/webfonts/fa-v4compatibility.ttf b/deps/font-awesome-6.4.2/webfonts/fa-v4compatibility.ttf
new file mode 100644
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new file mode 100644
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diff --git a/deps/headroom-0.11.0/headroom.min.js b/deps/headroom-0.11.0/headroom.min.js
new file mode 100644
index 0000000..433069f
--- /dev/null
+++ b/deps/headroom-0.11.0/headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.11.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=l<t?"down":"up",a.distance=Math.abs(t-l),a.isOutOfBounds=t<0||o<t+n,a.top=t<=s.offset,a.bottom=o<=t+n,a.toleranceExceeded=a.distance>s.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t,n){n=n||{},Object.assign(this,o.options,n),this.classes=Object.assign({},o.options.classes,n.classes),this.elem=t,this.tolerance=function(t){return t===Object(t)?t:{down:t,up:t}}(this.tolerance),this.initialised=!1,this.frozen=!1}return o.prototype={constructor:o,init:function(){return o.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},o.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},o.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),o});
\ No newline at end of file
diff --git a/deps/headroom-0.11.0/jQuery.headroom.min.js b/deps/headroom-0.11.0/jQuery.headroom.min.js
new file mode 100644
index 0000000..17f70c9
--- /dev/null
+++ b/deps/headroom-0.11.0/jQuery.headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.9.4 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery);
\ No newline at end of file
diff --git a/deps/search-1.0.0/autocomplete.jquery.min.js b/deps/search-1.0.0/autocomplete.jquery.min.js
new file mode 100644
index 0000000..fb63a6a
--- /dev/null
+++ b/deps/search-1.0.0/autocomplete.jquery.min.js
@@ -0,0 +1,7 @@
+/*!
+ * autocomplete.js 0.38.0
+ * https://github.com/algolia/autocomplete.js
+ * Copyright 2020 Algolia, Inc. and other contributors; Licensed MIT
+ */
+!function(a){function b(d){if(c[d])return c[d].exports;var e=c[d]={exports:{},id:d,loaded:!1};return a[d].call(e.exports,e,e.exports,b),e.loaded=!0,e.exports}var c={};b.m=a,b.c=c,b.p="",b(0)}([function(a,b,c){"use strict";a.exports=c(1)},function(a,b,c){"use strict";var d=c(2),e=c(3);d.element=e;var f=c(4);f.isArray=e.isArray,f.isFunction=e.isFunction,f.isObject=e.isPlainObject,f.bind=e.proxy,f.each=function(a,b){function c(a,c){return b(c,a)}e.each(a,c)},f.map=e.map,f.mixin=e.extend,f.Event=e.Event;var g,h,i,j=c(5),k=c(6);g=e.fn.autocomplete,h="aaAutocomplete",i={initialize:function(a,b){function c(){var c,d=e(this),f=new k({el:d});c=new j({input:d,eventBus:f,dropdownMenuContainer:a.dropdownMenuContainer,hint:void 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a=this.input.getQuery();this.dropdown.isEmpty&&a.length>=this.minLength?this.dropdown.update(a):this.dropdown.moveCursorDown(),this.dropdown.open()},_onLeftKeyed:function(){"rtl"===this.dir&&this._autocomplete()},_onRightKeyed:function(){"ltr"===this.dir&&this._autocomplete()},_onQueryChanged:function(a,b){this.input.clearHintIfInvalid(),b.length>=this.minLength?this.dropdown.update(b):this.dropdown.empty(),this.dropdown.open(),this._setLanguageDirection()},_onWhitespaceChanged:function(){this._updateHint(),this.dropdown.open()},_setLanguageDirection:function(){var a=this.input.getLanguageDirection();this.dir!==a&&(this.dir=a,this.$node.css("direction",a),this.dropdown.setLanguageDirection(a))},_updateHint:function(){var a,b,c,d,e,f;a=this.dropdown.getDatumForTopSuggestion(),a&&this.dropdown.isVisible()&&!this.input.hasOverflow()?(b=this.input.getInputValue(),c=l.normalizeQuery(b),d=i.escapeRegExChars(c),e=new 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c(a){return"<p>"+b(a)+"</p>"}return{empty:a.empty&&k.templatify(a.empty),header:a.header&&k.templatify(a.header),footer:a.footer&&k.templatify(a.footer),suggestion:a.suggestion||c}}function g(a){return/^[_a-zA-Z0-9-]+$/.test(a)}var h="aaDataset",i="aaValue",j="aaDatum",k=c(4),l=c(2),m=c(18),n=c(19),o=c(8);d.extractDatasetName=function(a){return l.element(a).data(h)},d.extractValue=function(a){return l.element(a).data(i)},d.extractDatum=function(a){var b=l.element(a).data(j);return"string"==typeof b&&(b=JSON.parse(b)),b},k.mixin(d.prototype,o,{_render:function(a,b){function c(){var b=[].slice.call(arguments,0);return b=[{query:a,isEmpty:!0}].concat(b),n.templates.empty.apply(this,b)}function d(){function a(a){var b,c=m.suggestion.replace("%PREFIX%",f.cssClasses.prefix).replace("%SUGGESTION%",f.cssClasses.suggestion);return b=l.element(c).attr({role:"option",id:["option",Math.floor(1e8*Math.random())].join("-")}).append(n.templates.suggestion.apply(this,[a].concat(e))),b.data(h,n.name),b.data(i,n.displayFn(a)||void 0),b.data(j,JSON.stringify(a)),b.children().each(function(){l.element(this).css(f.css.suggestionChild)}),b}var c,d,e=[].slice.call(arguments,0),f=this,g=m.suggestions.replace("%PREFIX%",this.cssClasses.prefix).replace("%SUGGESTIONS%",this.cssClasses.suggestions);return c=l.element(g).css(this.css.suggestions),d=k.map(b,a),c.append.apply(c,d),c}function e(){var b=[].slice.call(arguments,0);return b=[{query:a,isEmpty:!g}].concat(b),n.templates.header.apply(this,b)}function f(){var b=[].slice.call(arguments,0);return b=[{query:a,isEmpty:!g}].concat(b),n.templates.footer.apply(this,b)}if(this.$el){var 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+var c=[].slice.call(arguments,1);this.cacheSuggestions(a,b,c),this._render.apply(this,[a,b].concat(c))}}if(this.query=a,this.canceled=!1,this.shouldFetchFromCache(a))b.apply(this,[this.cachedSuggestions].concat(this.cachedRenderExtraArgs));else{var c=this,d=function(){c.canceled||c.source(a,b.bind(c))};if(this.debounce){var e=function(){c.debounceTimeout=null,d()};clearTimeout(this.debounceTimeout),this.debounceTimeout=setTimeout(e,this.debounce)}else d()}},cacheSuggestions:function(a,b,c){this.cachedQuery=a,this.cachedSuggestions=b,this.cachedRenderExtraArgs=c},shouldFetchFromCache:function(a){return this.cache&&this.cachedQuery===a&&this.cachedSuggestions&&this.cachedSuggestions.length},clearCachedSuggestions:function(){delete this.cachedQuery,delete this.cachedSuggestions,delete this.cachedRenderExtraArgs},cancel:function(){this.canceled=!0},clear:function(){this.$el&&(this.cancel(),this.$el.empty(),this.trigger("rendered",""))},isEmpty:function(){return this._isEmpty},destroy:function(){this.clearCachedSuggestions(),this.$el=null}}),a.exports=d},function(a,b){"use strict";a.exports={wrapper:'<span class="%ROOT%"></span>',dropdown:'<span class="%PREFIX%%DROPDOWN_MENU%"></span>',dataset:'<div class="%PREFIX%%DATASET%-%CLASS%"></div>',suggestions:'<span class="%PREFIX%%SUGGESTIONS%"></span>',suggestion:'<div class="%PREFIX%%SUGGESTION%"></div>'}},function(a,b,c){"use strict";var d=c(4),e={wrapper:{position:"relative",display:"inline-block"},hint:{position:"absolute",top:"0",left:"0",borderColor:"transparent",boxShadow:"none",opacity:"1"},input:{position:"relative",verticalAlign:"top",backgroundColor:"transparent"},inputWithNoHint:{position:"relative",verticalAlign:"top"},dropdown:{position:"absolute",top:"100%",left:"0",zIndex:"100",display:"none"},suggestions:{display:"block"},suggestion:{whiteSpace:"nowrap",cursor:"pointer"},suggestionChild:{whiteSpace:"normal"},ltr:{left:"0",right:"auto"},rtl:{left:"auto",right:"0"},defaultClasses:{root:"algolia-autocomplete",prefix:"aa",noPrefix:!1,dropdownMenu:"dropdown-menu",input:"input",hint:"hint",suggestions:"suggestions",suggestion:"suggestion",cursor:"cursor",dataset:"dataset",empty:"empty"},appendTo:{wrapper:{position:"absolute",zIndex:"100",display:"none"},input:{},inputWithNoHint:{},dropdown:{display:"block"}}};d.isMsie()&&d.mixin(e.input,{backgroundImage:"url(data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)"}),d.isMsie()&&d.isMsie()<=7&&d.mixin(e.input,{marginTop:"-1px"}),a.exports=e},function(a,b,c){"use strict";a.exports={hits:c(21),popularIn:c(24)}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23),g=function(){function a(a,d){return window.Promise.resolve().then(function(){return b.length&&(e=a.search(b),b=[]),e}).then(function(a){if(a)return c=a.results,c[d]})}var b=[],c=[],e=window.Promise.resolve();return function(c,e){return function(f,g){a(c.as,b.push({indexName:c.indexName,query:f,params:e})-1).then(function(a){a&&g(a.hits,a)}).catch(function(a){d.error(a.message)})}}}();a.exports=function(a,b){var c=f(a.as._ua);if(c&&c[0]>=3&&c[1]>20){var d="autocomplete.js "+e;a.as._ua.indexOf(d)===-1&&(a.as._ua+="; "+d)}return g(a,b)}},function(a,b){a.exports="0.38.0"},function(a,b){"use strict";a.exports=function(a){var b=a.match(/Algolia for JavaScript \((\d+\.)(\d+\.)(\d+)\)/)||a.match(/Algolia for vanilla JavaScript (\d+\.)(\d+\.)(\d+)/);if(b)return[b[1],b[2],b[3]]}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23);a.exports=function(a,b,c,g){function h(h,i){a.search(h,b,function(a,h){if(a)return void d.error(a.message);if(h.hits.length>0){var l=h.hits[0],m=d.mixin({hitsPerPage:0},c);delete m.source,delete m.index;var n=f(k.as._ua);return n&&n[0]>=3&&n[1]>20&&(b.additionalUA="autocomplete.js "+e),void k.search(j(l),m,function(a,b){if(a)return void d.error(a.message);var c=[];if(g.includeAll){var e=g.allTitle||"All departments";c.push(d.mixin({facet:{value:e,count:b.nbHits}},d.cloneDeep(l)))}d.each(b.facets,function(a,b){d.each(a,function(a,e){c.push(d.mixin({facet:{facet:b,value:e,count:a}},d.cloneDeep(l)))})});for(var f=1;f<h.hits.length;++f)c.push(h.hits[f]);i(c,h)})}i([])})}var i=f(a.as._ua);if(i&&i[0]>=3&&i[1]>20&&(b=b||{},b.additionalUA="autocomplete.js "+e),!c.source)return d.error("Missing 'source' key");var j=d.isFunction(c.source)?c.source:function(a){return a[c.source]};if(!c.index)return d.error("Missing 'index' key");var k=c.index;return g=g||{},h}}]);
\ No newline at end of file
diff --git a/deps/search-1.0.0/fuse.min.js b/deps/search-1.0.0/fuse.min.js
new file mode 100644
index 0000000..7def598
--- /dev/null
+++ b/deps/search-1.0.0/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.4.6 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2021 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var e,t;e=this,t=function(){"use strict";function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}function t(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function n(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}function r(e,t,r){return t&&n(e.prototype,t),r&&n(e,r),e}function i(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function o(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function c(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?o(Object(n),!0).forEach((function(t){i(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):o(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function a(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function");e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,writable:!0,configurable:!0}}),t&&u(e,t)}function s(e){return(s=Object.setPrototypeOf?Object.getPrototypeOf:function(e){return e.__proto__||Object.getPrototypeOf(e)})(e)}function u(e,t){return(u=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e})(e,t)}function h(e,t){return!t||"object"!=typeof t&&"function"!=typeof t?function(e){if(void 0===e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return e}(e):t}function f(e){var t=function(){if("undefined"==typeof 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diff --git a/deps/search-1.0.0/mark.min.js b/deps/search-1.0.0/mark.min.js
new file mode 100644
index 0000000..1eea053
--- /dev/null
+++ b/deps/search-1.0.0/mark.min.js
@@ -0,0 +1,7 @@
+/*!***************************************************
+* mark.js v8.11.1
+* https://markjs.io/
+* Copyright (c) 2014–2018, Julian Kühnel
+* Released under the MIT license https://git.io/vwTVl
+*****************************************************/
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):e.Mark=t()}(this,function(){"use strict";var e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},t=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},n=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),r=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},i=function(){function e(n){var r=!(arguments.length>1&&void 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diff --git a/index.html b/index.html
index 7a4610e..ee757ae 100644
--- a/index.html
+++ b/index.html
@@ -5,7 +5,6 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<meta name="description" content="Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber &amp; Kastner (2023) &lt;doi:10.48550/arXiv.2206.04902&gt;. Efficient equation-per-equation estimation following Kastner &amp; Huber (2020) &lt;doi:10.1002/for.2680&gt; and Carrerio et al. (2021) &lt;doi:10.1016/j.jeconom.2021.11.010&gt;.">
 <title>MCMC Estimation of Bayesian Vectorautoregressions • bayesianVARs</title>
 <!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png">
 <link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png">
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+<meta name="description" content="Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber &amp; Kastner (2023) &lt;doi:10.48550/arXiv.2206.04902&gt;. Efficient equation-per-equation estimation following Kastner &amp; Huber (2020) &lt;doi:10.1002/for.2680&gt; and Carrerio et al. (2021) &lt;doi:10.1016/j.jeconom.2021.11.010&gt;.">
 <meta property="og:description" content="Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber &amp; Kastner (2023) &lt;doi:10.48550/arXiv.2206.04902&gt;. Efficient equation-per-equation estimation following Kastner &amp; Huber (2020) &lt;doi:10.1002/for.2680&gt; and Carrerio et al. (2021) &lt;doi:10.1016/j.jeconom.2021.11.010&gt;.">
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@@ -81,7 +67,7 @@
 <!-- badges: start -->
 
 <p>Estimation of Bayesian vectorautoregressions with/without stochastic volatility.</p>
-<p>Implements several modern hierarchical shrinkage priors, amongst them Dirichlet-Laplace prior (DL), hierarchical Minnesota prior (HM), Horseshoe prior (HS), normal-gamma prior (NG), <span class="math inline"><em>R</em><sup>2</sup></span>-induced-Dirichlet-decomposition prior (R2D2) and stochastic search variable selection prior (SSVS).</p>
+<p>Implements several modern hierarchical shrinkage priors, amongst them Dirichlet-Laplace prior (DL), hierarchical Minnesota prior (HM), Horseshoe prior (HS), normal-gamma prior (NG), <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>R</mi><mn>2</mn></msup><annotation encoding="application/x-tex">R^2</annotation></semantics></math>-induced-Dirichlet-decomposition prior (R2D2) and stochastic search variable selection prior (SSVS).</p>
 <p>Concerning the error-term, the user can either specify an order-invariant factor structure or an order-variant cholesky structure.</p>
 </div>
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@@ -181,15 +167,15 @@ <h2 data-toc-skip>Dev status</h2>
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new file mode 100644
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new file mode 100644
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@@ -0,0 +1,85 @@
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+/*!
+ * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/)
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+const setStoredTheme = theme => localStorage.setItem('theme', theme)
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diff --git a/news/index.html b/news/index.html
index ddac3a9..742e6f0 100644
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/bayesianVARs-vignette.pdf">Shrinkage Priors for Bayesian Vectorautoregressions featuring Stochastic Volatility Using the **R** Package **bayesianVARs** (PDF)</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/luisgruber/bayesianVARs/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-news">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Changelog</h1>
-      <small>Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
+      <small>Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="development version" id="bayesianvars-development-version">bayesianVARs (development version)<a class="anchor" aria-label="anchor" href="#bayesianvars-development-version"></a></h2>
+<ul><li>For consistency with other functions, from now on prior_intercept in bvar() specifies standard deviations instead of variances.</li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.1.3" id="bayesianvars-013">bayesianVARs 0.1.3<a class="anchor" aria-label="anchor" href="#bayesianvars-013"></a></h2><p class="text-muted">CRAN release: 2024-06-25</p>
 <ul><li>bugfix concerning VAR with factor structure on errors with homoscedastic factors.</li>
@@ -69,7 +58,7 @@ <h2 class="pkg-version" data-toc-text="0.1.1" id="bayesianvars-011">bayesianVARs
 <h2 class="pkg-version" data-toc-text="0.1.0" id="bayesianvars-010">bayesianVARs 0.1.0<a class="anchor" aria-label="anchor" href="#bayesianvars-010"></a></h2><p class="text-muted">CRAN release: 2024-01-13</p>
 <ul><li>Initial CRAN submission.</li>
 </ul></div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -78,14 +67,14 @@ <h2 class="pkg-version" data-toc-text="0.1.0" id="bayesianvars-010">bayesianVARs
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
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diff --git a/pkgdown.js b/pkgdown.js
index 9bd6621..9757bf9 100644
--- a/pkgdown.js
+++ b/pkgdown.js
@@ -152,5 +152,3 @@ async function searchFuse(query, callback) {
   });
   });
 })(window.jQuery || window.$)
-
-
diff --git a/pkgdown.yml b/pkgdown.yml
index 2771c4c..dd519ee 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,10 +1,9 @@
 pandoc: 3.1.11
-pkgdown: 2.0.9
+pkgdown: 2.1.0
 pkgdown_sha: ~
 articles:
   bayesianVARs-vignette: bayesianVARs-vignette.pdf
-last_built: 2024-06-26T07:47Z
+last_built: 2024-08-29T19:40Z
 urls:
   reference: https://luisgruber.github.io/bayesianVARs/reference
   article: https://luisgruber.github.io/bayesianVARs/articles
-
diff --git a/reference/Rplot002.png b/reference/Rplot002.png
index 405a45901b1a2719fe5014c7f57c143cc2ef04d9..3416e2f051c411b140af3d12d3a39dd978db07c7 100644
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diff --git a/reference/bvar.html b/reference/bvar.html
index 73634cb..055fb8c 100644
--- a/reference/bvar.html
+++ b/reference/bvar.html
@@ -1,55 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="bvar simulates from the joint posterior distribution of the parameters
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
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       <img src="../logo.png" class="logo" alt=""><h1>Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/bvar_wrapper.R" class="external-link"><code>R/bvar_wrapper.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/bvar_wrapper.R" class="external-link"><code>R/bvar_wrapper.R</code></a></small>
       <div class="d-none name"><code>bvar.Rd</code></div>
     </div>
 
@@ -78,74 +63,74 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
+
+
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>Data matrix (can be a time series object). Each of \(M\) columns
 is assumed to contain a single time-series of length \(T\).</p></dd>
 
 
-<dt>lags</dt>
+<dt id="arg-lags">lags<a class="anchor" aria-label="anchor" href="#arg-lags"></a></dt>
 <dd><p>Integer indicating the order of the VAR, i.e. the number of lags
 of the dependent variables included as predictors.</p></dd>
 
 
-<dt>draws</dt>
+<dt id="arg-draws">draws<a class="anchor" aria-label="anchor" href="#arg-draws"></a></dt>
 <dd><p>single integer indicating the number of draws after the burnin</p></dd>
 
 
-<dt>burnin</dt>
+<dt id="arg-burnin">burnin<a class="anchor" aria-label="anchor" href="#arg-burnin"></a></dt>
 <dd><p>single integer indicating the number of draws discarded as
 burnin</p></dd>
 
 
-<dt>thin</dt>
+<dt id="arg-thin">thin<a class="anchor" aria-label="anchor" href="#arg-thin"></a></dt>
 <dd><p>single integer. Every \(thin\)th draw will be stored. Default is
 <code>thin=1L</code>.</p></dd>
 
 
-<dt>prior_intercept</dt>
+<dt id="arg-prior-intercept">prior_intercept<a class="anchor" aria-label="anchor" href="#arg-prior-intercept"></a></dt>
 <dd><p>Either <code>prior_intercept=FALSE</code> and no constant
 term (intercept) will be included. Or a numeric vector of length \(M\)
 indicating the (fixed) prior variances on the constant term. A single number
 will be recycled accordingly. Default is <code>prior_intercept=100</code>.</p></dd>
 
 
-<dt>prior_phi</dt>
+<dt id="arg-prior-phi">prior_phi<a class="anchor" aria-label="anchor" href="#arg-prior-phi"></a></dt>
 <dd><p><code>bayesianVARs_prior_phi</code> object specifying prior for the
 reduced form VAR coefficients. Best use constructor
 <code><a href="specify_prior_phi.html">specify_prior_phi</a></code>.</p></dd>
 
 
-<dt>prior_sigma</dt>
+<dt id="arg-prior-sigma">prior_sigma<a class="anchor" aria-label="anchor" href="#arg-prior-sigma"></a></dt>
 <dd><p><code>bayesianVARs_prior_sigma</code> object specifying prior for
 the variance-covariance matrix of the VAR. Best use constructor
 <code><a href="specify_prior_sigma.html">specify_prior_sigma</a></code>.</p></dd>
 
 
-<dt>sv_keep</dt>
+<dt id="arg-sv-keep">sv_keep<a class="anchor" aria-label="anchor" href="#arg-sv-keep"></a></dt>
 <dd><p>String equal to <code>"all"</code> or <code>"last"</code>. In case of
 <code>sv_keep = "last"</code>, the default, only draws for the very last
 log-variance \(h_T\) are stored.</p></dd>
 
 
-<dt>quiet</dt>
+<dt id="arg-quiet">quiet<a class="anchor" aria-label="anchor" href="#arg-quiet"></a></dt>
 <dd><p>logical value indicating whether information about the progress
 during sampling should be displayed during sampling (default is
 <code>TRUE</code>).</p></dd>
 
 
-<dt>startvals</dt>
+<dt id="arg-startvals">startvals<a class="anchor" aria-label="anchor" href="#arg-startvals"></a></dt>
 <dd><p>optional list with starting values.</p></dd>
 
 
-<dt>expert</dt>
+<dt id="arg-expert">expert<a class="anchor" aria-label="anchor" href="#arg-expert"></a></dt>
 <dd><p>optional list with expert settings.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An object of type <code>bayesianVARs_bvar</code>, a list containing the following
+    <p>An object of type <code>bayesianVARs_bvar</code>, a list containing the following
 objects:</p><ul><li><p><code>PHI</code>: A <code>bayesianVARs_coef</code> object, an array, containing the posterior draws
 of the VAR coefficients (including the intercept).</p></li>
 <li><p><code>U</code>: A <code>bayesianVARs_draws</code> object, a matrix, containing the posterior draws
@@ -238,11 +223,11 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 <a href="https://arxiv.org/abs/2206.04902" class="external-link">arXiv:2206.04902</a>.</p>
 <p>Kastner, G. and Huber, F. Sparse (2020). Bayesian vector
 autoregressions in huge dimensions. <em>Journal of Forecasting</em>.
-<b>39</b>, 1142--1165, <a href="https://doi.org/10.1002/for.2680" class="external-link">doi:10.1002/for.2680</a>
+<b>39</b>, 1142–1165, <a href="https://doi.org/10.1002/for.2680" class="external-link">doi:10.1002/for.2680</a>
 .</p>
 <p>Kastner, G. (2019). Sparse Bayesian Time-Varying Covariance
 Estimation in Many Dimensions <em>Journal of Econometrics</em>, <b>210</b>(1),
-98--115, <a href="https://doi.org/10.1016/j.jeconom.2018.11.007" class="external-link">doi:10.1016/j.jeconom.2018.11.007</a>
+98–115, <a href="https://doi.org/10.1016/j.jeconom.2018.11.007" class="external-link">doi:10.1016/j.jeconom.2018.11.007</a>
 .</p>
 <p>Carriero, A. and Chan, J. and  Clark, T. E. and Marcellino, M.
 (2021). Corrigendum to “Large Bayesian vector autoregressions with
@@ -252,7 +237,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 .</p>
 <p>Cogley, S. and Sargent, T. (2005). Drifts and volatilities:
 monetary policies and outcomes in the post WWII US. <em>Review of Economic
-Dynamics</em>, <b>8</b>, 262--302, <a href="https://doi.org/10.1016/j.red.2004.10.009" class="external-link">doi:10.1016/j.red.2004.10.009</a>
+Dynamics</em>, <b>8</b>, 262–302, <a href="https://doi.org/10.1016/j.red.2004.10.009" class="external-link">doi:10.1016/j.red.2004.10.009</a>
 .</p>
 <p>Hosszejni, D. and Kastner, G. (2021). Modeling Univariate and
 Multivariate Stochastic Volatility in R with stochvol and factorstochvol.
@@ -290,21 +275,21 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Posterior median of reduced-form coefficients:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                 GDPC1 CPIAUCSL   FEDFUNDS</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> GDPC1.l1     0.236588 0.006926  2.109e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> CPIAUCSL.l1 -0.053390 0.616162 -3.646e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FEDFUNDS.l1  0.007874 0.037088  1.001e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> intercept    0.005874 0.001291 -8.055e-05</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> GDPC1.l1     0.234251 0.006679  1.613e-02</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> CPIAUCSL.l1 -0.052490 0.609837 -2.106e-03</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> FEDFUNDS.l1  0.008550 0.038264  1.001e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> intercept    0.005904 0.001249 -6.616e-05</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Posterior interquartile range of of reduced-form coefficients:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                GDPC1  CPIAUCSL  FEDFUNDS</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> GDPC1.l1    0.089598 0.0290897 0.0252782</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> CPIAUCSL.l1 0.111515 0.0904426 0.0150976</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FEDFUNDS.l1 0.018416 0.0141909 0.0076726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> intercept   0.001135 0.0007528 0.0001651</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> GDPC1.l1    0.103406 0.0310374 0.0224336</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> CPIAUCSL.l1 0.112489 0.0972602 0.0136029</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> FEDFUNDS.l1 0.017590 0.0146875 0.0069129</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> intercept   0.001103 0.0007549 0.0001674</span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -313,14 +298,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/reference/coef.bayesianVARs_bvar.html b/reference/coef.bayesianVARs_bvar.html
new file mode 100644
index 0000000..a1f7ac0
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+++ b/reference/coef.bayesianVARs_bvar.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://luisgruber.github.io/bayesianVARs/reference/coef.html" />
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diff --git a/reference/coef.html b/reference/coef.html
index 83e38cf..a2c8bd0 100644
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+++ b/reference/coef.html
@@ -1,55 +1,40 @@
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+model estimated with bvar()."><meta property="og:description" content="Extracts posterior draws of the VAR coefficients from a VAR
+model estimated with bvar()."><meta property="og:image" content="https://luisgruber.github.io/bayesianVARs/logo.png"></head><body>
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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Extract VAR coefficients</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>coef.Rd</code></div>
     </div>
 
@@ -60,25 +45,25 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_bvar</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_bvar'</span></span>
 <span><span class="fu">coef</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A <code>bayesianVARs_bvar</code> object obtained from <code><a href="bvar.html">bvar()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns a numeric array of dimension \(M \times K \times draws\),
+    <p>Returns a numeric array of dimension \(M \times K \times draws\),
 where M is the number of time-series, K is the number of covariates per
 equation (including the intercept) and draws is the number of stored
 posterior draws.</p>
@@ -100,7 +85,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">bvar_coefs</span> <span class="op">&lt;-</span> <span class="fu">coef</span><span class="op">(</span><span class="va">mod</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -109,14 +94,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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diff --git a/reference/fitted.bayesianVARs_bvar.html b/reference/fitted.bayesianVARs_bvar.html
index 78be4fe..bb5d44a 100644
--- a/reference/fitted.bayesianVARs_bvar.html
+++ b/reference/fitted.bayesianVARs_bvar.html
@@ -1,53 +1,38 @@
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       <img src="../logo.png" class="logo" alt=""><h1>Simulate fitted/predicted historical values for an estimated VAR model</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>fitted.bayesianVARs_bvar.Rd</code></div>
     </div>
 
@@ -57,30 +42,30 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_bvar</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_bvar'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/fitted.values.html" class="external-link">fitted</a></span><span class="op">(</span><span class="va">object</span>, error_term <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A <code>bayesianVARs_bvar</code> object estimated via <code><a href="bvar.html">bvar()</a></code>.</p></dd>
 
 
-<dt>error_term</dt>
+<dt id="arg-error-term">error_term<a class="anchor" aria-label="anchor" href="#arg-error-term"></a></dt>
 <dd><p>logical indicating whether to include the error term or
 not.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An object of class <code>bayesianVARs_fitted</code>.</p>
+    <p>An object of class <code>bayesianVARs_fitted</code>.</p>
     </div>
 
     <div class="section level2">
@@ -104,7 +89,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="fitted.bayesianVARs_bvar-2.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -113,14 +98,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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-      <img src="../logo.png" class="logo" alt=""><h1>Function reference</h1>
+      <img src="../logo.png" class="logo" alt=""><h1>Package index</h1>
     </div>
 
     <div class="section level2">
       <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="#all-functions"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="bvar.html">bvar()</a></code> 
+
+          <code><a href="bvar.html">bvar()</a></code>
+
         </dt>
         <dd>Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions</dd>
       </dl><dl><dt>
-          
-          <code><a href="coef.html">coef(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code> 
+
+          <code><a href="coef.html">coef(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code>
+
         </dt>
         <dd>Extract VAR coefficients</dd>
       </dl><dl><dt>
-          
-          <code><a href="fitted.bayesianVARs_bvar.html">fitted(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code> 
+
+          <code><a href="fitted.bayesianVARs_bvar.html">fitted(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code>
+
         </dt>
         <dd>Simulate fitted/predicted historical values for an estimated VAR model</dd>
       </dl><dl><dt>
-          
-          <code><a href="my_gig.html">my_gig()</a></code> 
+
+          <code><a href="my_gig.html">my_gig()</a></code>
+
         </dt>
         <dd>Draw from generalized inverse Gaussian</dd>
       </dl><dl><dt>
-          
-          <code><a href="pairs_predict.html">pairs(<i>&lt;bayesianVARs_predict&gt;</i>)</a></code> 
+
+          <code><a href="pairs_predict.html">pairs(<i>&lt;bayesianVARs_predict&gt;</i>)</a></code>
+
         </dt>
         <dd>Pairwise visualization of out-of-sample posterior predictive densities.</dd>
       </dl><dl><dt>
-          
-          <code><a href="plot.bayesianVARs_bvar.html">plot(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code> 
+
+          <code><a href="plot.bayesianVARs_bvar.html">plot(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code>
+
         </dt>
         <dd>Plot method for bayesianVARs_bvar</dd>
       </dl><dl><dt>
-          
-          <code><a href="plot.bayesianVARs_fitted.html">plot(<i>&lt;bayesianVARs_fitted&gt;</i>)</a></code> 
+
+          <code><a href="plot.bayesianVARs_fitted.html">plot(<i>&lt;bayesianVARs_fitted&gt;</i>)</a></code>
+
         </dt>
         <dd>Visualization of in-sample fit of an estimated VAR.</dd>
       </dl><dl><dt>
-          
-          <code><a href="plot.bayesianVARs_predict.html">plot(<i>&lt;bayesianVARs_predict&gt;</i>)</a></code> 
+
+          <code><a href="plot.bayesianVARs_predict.html">plot(<i>&lt;bayesianVARs_predict&gt;</i>)</a></code>
+
         </dt>
         <dd>Fan chart</dd>
       </dl><dl><dt>
-          
-          <code><a href="posterior_heatmap.html">posterior_heatmap()</a></code> 
+
+          <code><a href="posterior_heatmap.html">posterior_heatmap()</a></code>
+
         </dt>
         <dd>Posterior heatmaps for VAR coefficients or variance-covariance matrices</dd>
       </dl><dl><dt>
-          
-          <code><a href="predict.bayesianVARs_bvar.html">predict(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code> 
+
+          <code><a href="predict.bayesianVARs_bvar.html">predict(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code>
+
         </dt>
         <dd>Predict method for Bayesian VARs</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.bayesianVARs_bvar.html">print(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code> 
+
+          <code><a href="print.bayesianVARs_bvar.html">print(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code>
+
         </dt>
         <dd>Pretty printing of a bvar object</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.bayesianVARs_predict.html">print(<i>&lt;bayesianVARs_predict&gt;</i>)</a></code> 
+
+          <code><a href="print.bayesianVARs_predict.html">print(<i>&lt;bayesianVARs_predict&gt;</i>)</a></code>
+
         </dt>
         <dd>Print method for bayesianVARs_predict objects</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.summary.bayesianVARs_bvar.html">print(<i>&lt;summary.bayesianVARs_bvar&gt;</i>)</a></code> 
+
+          <code><a href="print.summary.bayesianVARs_bvar.html">print(<i>&lt;summary.bayesianVARs_bvar&gt;</i>)</a></code>
+
         </dt>
         <dd>Print method for summary.bayesianVARs_bvar objects</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.summary.bayesianVARs_predict.html">print(<i>&lt;summary.bayesianVARs_predict&gt;</i>)</a></code> 
+
+          <code><a href="print.summary.bayesianVARs_predict.html">print(<i>&lt;summary.bayesianVARs_predict&gt;</i>)</a></code>
+
         </dt>
         <dd>Print method for summary.bayesianVARs_predict objects</dd>
       </dl><dl><dt>
-          
-          <code><a href="specify_prior_phi.html">specify_prior_phi()</a></code> 
+
+          <code><a href="specify_prior_phi.html">specify_prior_phi()</a></code>
+
         </dt>
         <dd>Specify prior on PHI</dd>
       </dl><dl><dt>
-          
-          <code><a href="specify_prior_sigma.html">specify_prior_sigma()</a></code> 
+
+          <code><a href="specify_prior_sigma.html">specify_prior_sigma()</a></code>
+
         </dt>
         <dd>Specify prior on Sigma</dd>
       </dl><dl><dt>
-          
-          <code><a href="stable_bvar.html">stable_bvar()</a></code> 
+
+          <code><a href="stable_bvar.html">stable_bvar()</a></code>
+
         </dt>
         <dd>Stable posterior draws</dd>
       </dl><dl><dt>
-          
-          <code><a href="sub-.bayesianVARs_coef.html">`[`(<i>&lt;bayesianVARs_coef&gt;</i>)</a></code> 
+
+          <code><a href="sub-.bayesianVARs_coef.html">`[`(<i>&lt;bayesianVARs_coef&gt;</i>)</a></code>
+
         </dt>
         <dd>Extract or Replace Parts of a bayesianVARs_coef object</dd>
       </dl><dl><dt>
-          
-          <code><a href="sub-.bayesianVARs_draws.html">`[`(<i>&lt;bayesianVARs_draws&gt;</i>)</a></code> 
+
+          <code><a href="sub-.bayesianVARs_draws.html">`[`(<i>&lt;bayesianVARs_draws&gt;</i>)</a></code>
+
         </dt>
         <dd>Extract or Replace Parts of a bayesianVARs_draws object</dd>
       </dl><dl><dt>
-          
-          <code><a href="summary.bayesianVARs_bvar.html">summary(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code> 
+
+          <code><a href="summary.bayesianVARs_bvar.html">summary(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code>
+
         </dt>
         <dd>Summary method for bayesianVARs_bvar objects</dd>
       </dl><dl><dt>
-          
-          <code><a href="summary.bayesianVARs_draws.html">summary(<i>&lt;bayesianVARs_draws&gt;</i>)</a></code> 
+
+          <code><a href="summary.bayesianVARs_draws.html">summary(<i>&lt;bayesianVARs_draws&gt;</i>)</a></code>
+
         </dt>
         <dd>Summary statistics for bayesianVARs posterior draws.</dd>
       </dl><dl><dt>
-          
-          <code><a href="summary.bayesianVARs_predict.html">summary(<i>&lt;bayesianVARs_predict&gt;</i>)</a></code> 
+
+          <code><a href="summary.bayesianVARs_predict.html">summary(<i>&lt;bayesianVARs_predict&gt;</i>)</a></code>
+
         </dt>
         <dd>Summary method for bayesianVARs_predict objects</dd>
       </dl><dl><dt>
-          
-          <code><a href="usmacro_growth.html">usmacro_growth</a></code> 
+
+          <code><a href="usmacro_growth.html">usmacro_growth</a></code>
+
         </dt>
         <dd>Data from the US-economy</dd>
       </dl><dl><dt>
-          
-          <code><a href="vcov.bayesianVARs_bvar.html">vcov(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code> 
+
+          <code><a href="vcov.bayesianVARs_bvar.html">vcov(<i>&lt;bayesianVARs_bvar&gt;</i>)</a></code>
+
         </dt>
         <dd>Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model</dd>
       </dl></div>
@@ -189,14 +198,14 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/my_gig.html b/reference/my_gig.html
index 577c7e2..16369aa 100644
--- a/reference/my_gig.html
+++ b/reference/my_gig.html
@@ -1,53 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Vectorized version of rgig"><title>Draw from generalized inverse Gaussian — my_gig • bayesianVARs</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Draw from generalized inverse Gaussian — my_gig"><meta property="og:description" content="Vectorized version of rgig"><meta property="og:image" content="https://luisgruber.github.io/bayesianVARs/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Draw from generalized inverse Gaussian — my_gig • bayesianVARs</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Draw from generalized inverse Gaussian — my_gig"><meta name="description" content="Vectorized version of rgig"><meta property="og:description" content="Vectorized version of rgig"><meta property="og:image" content="https://luisgruber.github.io/bayesianVARs/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light" data-bs-theme="light"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">bayesianVARs</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3.9000</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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-    <a class="dropdown-item" href="../articles/bayesianVARs-vignette.pdf">Shrinkage Priors for Bayesian Vectorautoregressions featuring Stochastic Volatility Using the **R** Package **bayesianVARs** (PDF)</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/bayesianVARs-vignette.pdf">Shrinkage Priors for Bayesian Vectorautoregressions featuring Stochastic Volatility Using the **R** Package **bayesianVARs** (PDF)</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/luisgruber/bayesianVARs/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Draw from generalized inverse Gaussian</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/RcppExports.R" class="external-link"><code>R/RcppExports.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/RcppExports.R" class="external-link"><code>R/RcppExports.R</code></a></small>
       <div class="d-none name"><code>my_gig.Rd</code></div>
     </div>
 
@@ -62,27 +47,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>n</dt>
+
+
+<dl><dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>A single integer indicating the number of draws to generate.</p></dd>
 
 
-<dt>lambda</dt>
+<dt id="arg-lambda">lambda<a class="anchor" aria-label="anchor" href="#arg-lambda"></a></dt>
 <dd><p>vector of shape parameters.</p></dd>
 
 
-<dt>chi</dt>
+<dt id="arg-chi">chi<a class="anchor" aria-label="anchor" href="#arg-chi"></a></dt>
 <dd><p>vector of shape/scale parameters. Must be nonnegative for positive lambdas and positive else.</p></dd>
 
 
-<dt>psi</dt>
+<dt id="arg-psi">psi<a class="anchor" aria-label="anchor" href="#arg-psi"></a></dt>
 <dd><p>vector of shape/scale parameters. Must be nonnegative for negative lambdas and positive else.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Matrix of dimension <code>c(n,m)</code>, where <code>m</code> is the maximum length of <code>lambda</code>, <code>psi</code> and <code>chi</code>.</p>
+    <p>Matrix of dimension <code>c(n,m)</code>, where <code>m</code> is the maximum length of <code>lambda</code>, <code>psi</code> and <code>chi</code>.</p>
     </div>
 
     <div class="section level2">
@@ -90,7 +75,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">gigsamples</span> <span class="op">&lt;-</span> <span class="fu">my_gig</span><span class="op">(</span><span class="fl">2</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -99,14 +84,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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-  
 
-  
+
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   </body></html>
 
diff --git a/reference/pairs.bayesianVARs_predict.html b/reference/pairs.bayesianVARs_predict.html
new file mode 100644
index 0000000..5c5c25a
--- /dev/null
+++ b/reference/pairs.bayesianVARs_predict.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html">
+  </head>
+</html>
+
diff --git a/reference/pairs_predict-1.png b/reference/pairs_predict-1.png
index 15e055ee7933d2ebae189793a33b13df14c1a0b3..63f57b0da15e07b383da6c311093e4b4733c7ad6 100644
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diff --git a/reference/pairs_predict.html b/reference/pairs_predict.html
index 405e434..02db260 100644
--- a/reference/pairs_predict.html
+++ b/reference/pairs_predict.html
@@ -1,55 +1,40 @@
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+densities."><meta property="og:description" content="Pairwise visualization of out-of-sample posterior predictive
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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Pairwise visualization of out-of-sample posterior predictive densities.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/plots.R" class="external-link"><code>R/plots.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/plots.R" class="external-link"><code>R/plots.R</code></a></small>
       <div class="d-none name"><code>pairs_predict.Rd</code></div>
     </div>
 
@@ -60,37 +45,37 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_predict</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_predict'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/pairs.html" class="external-link">pairs</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">vars</span>, <span class="va">ahead</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object of class <code>bayesianVARs_predict</code> obtained via
 <code><a href="predict.bayesianVARs_bvar.html">predict.bayesianVARs_bvar()</a></code>.</p></dd>
 
 
-<dt>vars</dt>
+<dt id="arg-vars">vars<a class="anchor" aria-label="anchor" href="#arg-vars"></a></dt>
 <dd><p>Integer vector (or coercible to such) indicating which variables
 to plot.</p></dd>
 
 
-<dt>ahead</dt>
+<dt id="arg-ahead">ahead<a class="anchor" aria-label="anchor" href="#arg-ahead"></a></dt>
 <dd><p>Integer vector (or coercible to such) indicating which step
 ahead to plot. <code>max(ahead)</code> must be smaller equal to
 <code>dim(x$predictions)[1]</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored!</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns <code>x</code> invisibly.</p>
+    <p>Returns <code>x</code> invisibly.</p>
     </div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
@@ -116,7 +101,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           draws, that do not fulfill the stable criterion.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  485 stable posterior draws remaining for prediction!</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  526 stable posterior draws remaining for prediction!</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Visualize</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/pairs.html" class="external-link">pairs</a></span><span class="op">(</span><span class="va">predictions</span>, vars <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">3</span>, ahead <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span></span>
@@ -125,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="pairs_predict-3.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -134,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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diff --git a/reference/plot.bayesianVARs_bvar.html b/reference/plot.bayesianVARs_bvar.html
index adcaf12..8d4f230 100644
--- a/reference/plot.bayesianVARs_bvar.html
+++ b/reference/plot.bayesianVARs_bvar.html
@@ -1,55 +1,40 @@
 <!DOCTYPE html>
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-intervals of future values."><meta property="og:image" content="https://luisgruber.github.io/bayesianVARs/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar • bayesianVARs</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar"><meta name="description" content="Visualization of in-sample fit. Can also be used to display prediction
+intervals of future values."><meta property="og:description" content="Visualization of in-sample fit. Can also be used to display prediction
+intervals of future values."><meta property="og:image" content="https://luisgruber.github.io/bayesianVARs/logo.png"></head><body>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Plot method for bayesianVARs_bvar</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/plots.R" class="external-link"><code>R/plots.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/plots.R" class="external-link"><code>R/plots.R</code></a></small>
       <div class="d-none name"><code>plot.bayesianVARs_bvar.Rd</code></div>
     </div>
 
@@ -60,7 +45,7 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_bvar</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_bvar'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  predictions <span class="op">=</span> <span class="cn">NULL</span>,</span>
@@ -73,37 +58,37 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object of class <code>bayesianVARs_bvar</code> obtained via <code><a href="bvar.html">bvar()</a></code>.</p></dd>
 
 
-<dt>predictions</dt>
+<dt id="arg-predictions">predictions<a class="anchor" aria-label="anchor" href="#arg-predictions"></a></dt>
 <dd><p>Optional array of out of sample predictions, e.g. obtained
 via <code><a href="predict.bayesianVARs_bvar.html">predict.bayesianVARs_bvar()</a></code>.</p></dd>
 
 
-<dt>quantiles</dt>
+<dt id="arg-quantiles">quantiles<a class="anchor" aria-label="anchor" href="#arg-quantiles"></a></dt>
 <dd><p>numeric vector indicating which quantiles to plot.</p></dd>
 
 
-<dt>dates</dt>
+<dt id="arg-dates">dates<a class="anchor" aria-label="anchor" href="#arg-dates"></a></dt>
 <dd><p>optional vector of dates for labelling the x-axis. The default
 values is <code>NULL</code>; in this case, the axis will be labeled with numbers.</p></dd>
 
 
-<dt>n_col</dt>
+<dt id="arg-n-col">n_col<a class="anchor" aria-label="anchor" href="#arg-n-col"></a></dt>
 <dd><p>integer indicating the number of columns to use for plotting.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored!</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns <code>x</code> invisibly.</p>
+    <p>Returns <code>x</code> invisibly.</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -125,14 +110,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           draws, that do not fulfill the stable criterion.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  626 stable posterior draws remaining for prediction!</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  428 stable posterior draws remaining for prediction!</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Visualize</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mod</span>, predictions <span class="op">=</span> <span class="va">predictions</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="plot.bayesianVARs_bvar-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -141,14 +126,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/plot.bayesianVARs_fitted.html b/reference/plot.bayesianVARs_fitted.html
index ab348b8..7ffa49c 100644
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@@ -1,53 +1,38 @@
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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Visualization of in-sample fit of an estimated VAR.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/plots.R" class="external-link"><code>R/plots.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/plots.R" class="external-link"><code>R/plots.R</code></a></small>
       <div class="d-none name"><code>plot.bayesianVARs_fitted.Rd</code></div>
     </div>
 
@@ -57,7 +42,7 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_fitted</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_fitted'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  dates <span class="op">=</span> <span class="cn">NULL</span>,</span>
@@ -70,38 +55,38 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A <code>bayesianVARs_fitted</code> object.</p></dd>
 
 
-<dt>dates</dt>
+<dt id="arg-dates">dates<a class="anchor" aria-label="anchor" href="#arg-dates"></a></dt>
 <dd><p>optional vector of dates for labelling the x-axis. The default
 values is <code>NULL</code>; in this case, the axis will be labeled with numbers.</p></dd>
 
 
-<dt>vars</dt>
+<dt id="arg-vars">vars<a class="anchor" aria-label="anchor" href="#arg-vars"></a></dt>
 <dd><p>character vector containing the names of the variables to be
 visualized. The default is <code>"all"</code> indicating that the fit of all variables
 is visualized.</p></dd>
 
 
-<dt>quantiles</dt>
+<dt id="arg-quantiles">quantiles<a class="anchor" aria-label="anchor" href="#arg-quantiles"></a></dt>
 <dd><p>numeric vector indicating which quantiles to plot.</p></dd>
 
 
-<dt>n_col</dt>
+<dt id="arg-n-col">n_col<a class="anchor" aria-label="anchor" href="#arg-n-col"></a></dt>
 <dd><p>integer indicating the number of columns to use for plotting.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>returns <code>x</code> invisibly</p>
+    <p>returns <code>x</code> invisibly</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -129,7 +114,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="plot.bayesianVARs_fitted-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -138,14 +123,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
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+++ b/reference/plot.bayesianVARs_predict.html
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       <img src="../logo.png" class="logo" alt=""><h1>Fan chart</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/plots.R" class="external-link"><code>R/plots.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/plots.R" class="external-link"><code>R/plots.R</code></a></small>
       <div class="d-none name"><code>plot.bayesianVARs_predict.Rd</code></div>
     </div>
 
@@ -57,7 +42,7 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_predict</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_predict'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  dates <span class="op">=</span> <span class="cn">NULL</span>,</span>
@@ -72,49 +57,49 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object of type <code>bayesianVARs_predict</code> obtained via
 <code><a href="predict.bayesianVARs_bvar.html">predict.bayesianVARs_bvar()</a></code>.</p></dd>
 
 
-<dt>dates</dt>
+<dt id="arg-dates">dates<a class="anchor" aria-label="anchor" href="#arg-dates"></a></dt>
 <dd><p>optional vector of dates for labeling the x-axis. The default
 values is <code>NULL</code>; in this case, the axis will be labeled with numbers.</p></dd>
 
 
-<dt>vars</dt>
+<dt id="arg-vars">vars<a class="anchor" aria-label="anchor" href="#arg-vars"></a></dt>
 <dd><p>character vector containing the names of the variables to be
 visualized. The default is <code>"all"</code> indicating that all variables are
 visualized.</p></dd>
 
 
-<dt>ahead</dt>
+<dt id="arg-ahead">ahead<a class="anchor" aria-label="anchor" href="#arg-ahead"></a></dt>
 <dd><p>Integer vector (or coercible to such) indicating which step
 ahead to plot. <code>max(ahead)</code> must be smaller equal to
 <code>dim(x$predictions)[1]</code>.</p></dd>
 
 
-<dt>quantiles</dt>
+<dt id="arg-quantiles">quantiles<a class="anchor" aria-label="anchor" href="#arg-quantiles"></a></dt>
 <dd><p>numeric vector indicating which quantiles to plot.</p></dd>
 
 
-<dt>n_col</dt>
+<dt id="arg-n-col">n_col<a class="anchor" aria-label="anchor" href="#arg-n-col"></a></dt>
 <dd><p>integer indicating the number of columns to use for plotting.</p></dd>
 
 
-<dt>first_obs</dt>
+<dt id="arg-first-obs">first_obs<a class="anchor" aria-label="anchor" href="#arg-first-obs"></a></dt>
 <dd><p>integer indicating the first observation to be used for plotting.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored!</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns <code>x</code> invisibly!</p>
+    <p>Returns <code>x</code> invisibly!</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -136,14 +121,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           draws, that do not fulfill the stable criterion.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  385 stable posterior draws remaining for prediction!</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  433 stable posterior draws remaining for prediction!</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Visualize</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">predictions</span>, vars <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">3</span>, ahead <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="plot.bayesianVARs_predict-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -152,14 +137,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/reference/posterior_heatmap.html b/reference/posterior_heatmap.html
index 4be0224..d91333f 100644
--- a/reference/posterior_heatmap.html
+++ b/reference/posterior_heatmap.html
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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Posterior heatmaps for VAR coefficients or variance-covariance matrices</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/plots.R" class="external-link"><code>R/plots.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/plots.R" class="external-link"><code>R/plots.R</code></a></small>
       <div class="d-none name"><code>posterior_heatmap.Rd</code></div>
     </div>
 
@@ -77,83 +62,83 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An array of dimension \(a \times b \times draws\), where \(a
   \times b\) is the dimension of the parameter to visualize and draws is the
 number of posterior draws.</p></dd>
 
 
-<dt>FUN</dt>
+<dt id="arg-fun">FUN<a class="anchor" aria-label="anchor" href="#arg-fun"></a></dt>
 <dd><p>The summary function to be applied to margins <code>c(1,2)</code> of x. E.g.
 <code>"median"</code>, <code>"mean"</code>, <code>"IQR"</code>, <code>"sd"</code> or <code>"var"</code>.
 <code>apply(x, 1:2, FUN, ...)</code> must return a matrix!</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>optional arguments to <code>FUN</code>.</p></dd>
 
 
-<dt>colorbar</dt>
+<dt id="arg-colorbar">colorbar<a class="anchor" aria-label="anchor" href="#arg-colorbar"></a></dt>
 <dd><p>logical indicating whether to display a colorbar or not.
 Default is <code>TRUE</code>.</p></dd>
 
 
-<dt>xlabels</dt>
+<dt id="arg-xlabels">xlabels<a class="anchor" aria-label="anchor" href="#arg-xlabels"></a></dt>
 <dd><p><code>ylabels=NULL</code>, the default, indicates that the names of
 the dependent variables will be displayed. <code>ylabels=""</code> indicates that
 no ylabels will be displayed.</p></dd>
 
 
-<dt>ylabels</dt>
+<dt id="arg-ylabels">ylabels<a class="anchor" aria-label="anchor" href="#arg-ylabels"></a></dt>
 <dd><p><code>xlabels=NULL</code>, the default, indicates that the labels of
 all covariables (the lagged values of the dependent variables) will be
 displayed. <code>xlabels="lags"</code> indicates that only the lags will be
 marked. <code>xlabels=""</code> indicates that no ylabels are displayed.</p></dd>
 
 
-<dt>add_numbers</dt>
+<dt id="arg-add-numbers">add_numbers<a class="anchor" aria-label="anchor" href="#arg-add-numbers"></a></dt>
 <dd><p>logical. <code>add_numbers=TRUE</code>, the default indicates
 that the actual values of <code>summary</code> will be displayed.</p></dd>
 
 
-<dt>zlim</dt>
+<dt id="arg-zlim">zlim<a class="anchor" aria-label="anchor" href="#arg-zlim"></a></dt>
 <dd><p>numeric vector of length two indicating the minimum and maximum
 values for which colors should be plotted. By default this range is
 determined by the maximum of the absolute values of the selected summary.</p></dd>
 
 
-<dt>colspace</dt>
+<dt id="arg-colspace">colspace<a class="anchor" aria-label="anchor" href="#arg-colspace"></a></dt>
 <dd><p>Optional argument.</p></dd>
 
 
-<dt>main</dt>
+<dt id="arg-main">main<a class="anchor" aria-label="anchor" href="#arg-main"></a></dt>
 <dd><p>main title for the plot.</p></dd>
 
 
-<dt>cex.axis</dt>
+<dt id="arg-cex-axis">cex.axis<a class="anchor" aria-label="anchor" href="#arg-cex-axis"></a></dt>
 <dd><p>The magnification to be used for y-axis annotation relative
 to the current setting of cex.</p></dd>
 
 
-<dt>cex.colbar</dt>
+<dt id="arg-cex-colbar">cex.colbar<a class="anchor" aria-label="anchor" href="#arg-cex-colbar"></a></dt>
 <dd><p>The magnification to be used for colorbar annotation
 relative to the current setting of cex.</p></dd>
 
 
-<dt>cex.numbers</dt>
+<dt id="arg-cex-numbers">cex.numbers<a class="anchor" aria-label="anchor" href="#arg-cex-numbers"></a></dt>
 <dd><p>The magnification to be used for the actual values (if
 <code>add_numbers=TRUE</code>) relative to the current setting of cex.</p></dd>
 
 
-<dt>asp</dt>
+<dt id="arg-asp">asp<a class="anchor" aria-label="anchor" href="#arg-asp"></a></dt>
 <dd><p>aspect ratio.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns <code>x</code> invisibly.</p>
+    <p>Returns <code>x</code> invisibly.</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -184,7 +169,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="posterior_heatmap-2.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -193,14 +178,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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diff --git a/reference/predict.bayesianVARs_bvar.html b/reference/predict.bayesianVARs_bvar.html
index edba7d6..1b766f7 100644
--- a/reference/predict.bayesianVARs_bvar.html
+++ b/reference/predict.bayesianVARs_bvar.html
@@ -1,57 +1,42 @@
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       <img src="../logo.png" class="logo" alt=""><h1>Predict method for Bayesian VARs</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>predict.bayesianVARs_bvar.Rd</code></div>
     </div>
 
@@ -63,7 +48,7 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_bvar</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_bvar'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  ahead <span class="op">=</span> <span class="fl">1L</span>,</span>
@@ -79,58 +64,58 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A <code>bayesianVARs_bvar</code> object, obtained from <code><a href="bvar.html">bvar()</a></code>.</p></dd>
 
 
-<dt>ahead</dt>
+<dt id="arg-ahead">ahead<a class="anchor" aria-label="anchor" href="#arg-ahead"></a></dt>
 <dd><p>Integer vector (or coercible to such), indicating the number of
 steps ahead at which to predict.</p></dd>
 
 
-<dt>each</dt>
+<dt id="arg-each">each<a class="anchor" aria-label="anchor" href="#arg-each"></a></dt>
 <dd><p>Single integer (or coercible to such) indicating how often should
 be drawn from the posterior predictive distribution for each draw that has
 been stored during MCMC sampling.</p></dd>
 
 
-<dt>stable</dt>
+<dt id="arg-stable">stable<a class="anchor" aria-label="anchor" href="#arg-stable"></a></dt>
 <dd><p>logical indicating whether to consider only those draws from
 the posterior that fulfill the 'stable' criterion. Default is <code>TRUE</code>.</p></dd>
 
 
-<dt>simulate_predictive</dt>
+<dt id="arg-simulate-predictive">simulate_predictive<a class="anchor" aria-label="anchor" href="#arg-simulate-predictive"></a></dt>
 <dd><p>logical, indicating whether the posterior
 predictive distribution should be simulated.</p></dd>
 
 
-<dt>LPL</dt>
+<dt id="arg-lpl">LPL<a class="anchor" aria-label="anchor" href="#arg-lpl"></a></dt>
 <dd><p>logical indicating whether <code>ahead</code>-step-ahead log predictive
 likelihoods should be computed. If <code>LPL=TRUE</code>, <code>Y_obs</code> has to be
 specified.</p></dd>
 
 
-<dt>Y_obs</dt>
+<dt id="arg-y-obs">Y_obs<a class="anchor" aria-label="anchor" href="#arg-y-obs"></a></dt>
 <dd><p>Data matrix of observed values for computation of log predictive
 likelihood. Each of <code>ncol(object$Yraw)</code> columns is assumed to contain a
 single time-series of length <code>length(ahead)</code>.</p></dd>
 
 
-<dt>LPL_VoI</dt>
+<dt id="arg-lpl-voi">LPL_VoI<a class="anchor" aria-label="anchor" href="#arg-lpl-voi"></a></dt>
 <dd><p>either integer vector or character vector of column-names
 indicating for which subgroup of time-series in <code>object$Yraw</code> a joint log
 predictive likelihood shall be computed.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored!</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Object of class <code>bayesianVARs_predict</code>, a list that may contain the
+    <p>Object of class <code>bayesianVARs_predict</code>, a list that may contain the
 following elements:</p><ul><li><p><code>predictions</code> array of dimensions
 <code>c(length(ahead), ncol(object$Yraw), each * dim(object$PHI)[3])</code>
 containing the simulations from the predictive density (if
@@ -176,43 +161,43 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           draws, that do not fulfill the stable criterion.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  617 stable posterior draws remaining for prediction!</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  643 stable posterior draws remaining for prediction!</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Summary</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">predictions</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> LPL:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   t+1   t+2   t+3   t+4 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5.114 9.665 9.092 6.526 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2.718 8.556 8.419 6.221 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Marginal univariate LPLs:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       GDPC1 CPIAUCSL FEDFUNDS</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> t+1 -0.8729   0.4109    3.541</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> t+2  2.8555   2.6739    3.391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> t+3  2.8363   2.4370    3.349</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> t+4  2.8931   0.2959    3.326</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      GDPC1 CPIAUCSL FEDFUNDS</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> t+1 -1.226   0.5816    3.554</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> t+2  2.479   2.7464    3.248</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> t+3  2.622   2.5796    3.081</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> t+4  2.797   0.6113    2.936</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Prediction quantiles:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , GDPC1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          t+1        t+2       t+3       t+4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.07875 -0.0329135 -0.021393 -0.011870</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.01851  0.0009099  0.005688  0.006618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.03798  0.0429166  0.034118  0.024602</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          t+1       t+2       t+3       t+4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.07047 -0.039599 -0.023225 -0.017323</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.02556 -0.004664  0.003999  0.005908</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.01933  0.030542  0.035271  0.027565</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , CPIAUCSL</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           t+1       t+2       t+3        t+4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.018515 -0.017824 -0.015592 -0.0140807</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.007656 -0.005287 -0.002413 -0.0005886</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.002679  0.006747  0.009743  0.0106464</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.019737 -0.019905 -0.018104 -0.0142483</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.008343 -0.005330 -0.003288 -0.0008071</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.003750  0.007712  0.009475  0.0122323</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , FEDFUNDS</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           t+1       t+2       t+3       t+4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.016214 -0.022544 -0.026463 -0.030055</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.004527 -0.005249 -0.006258 -0.006404</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.005912  0.011279  0.016238  0.018100</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.021391 -0.031018 -0.045467 -0.050481</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.004621 -0.005998 -0.006405 -0.007743</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.012474  0.021421  0.032884  0.032841</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Visualize via fan-charts</span></span></span>
@@ -226,14 +211,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           draws, that do not fulfill the stable criterion.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  617 stable posterior draws remaining for prediction!</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  643 stable posterior draws remaining for prediction!</span>
 <span class="r-in"><span><span class="va">predictions</span><span class="op">$</span><span class="va">LPL_VoI</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>      t+1      t+2      t+3      t+4 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2.542386 6.712653 6.548112 6.391536 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2.345726 5.790619 5.785145 5.795078 </span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -242,14 +227,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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index 0c8a4c1..fda719f 100644
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+++ b/reference/print.bayesianVARs_bvar.html
@@ -1,53 +1,38 @@
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+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Pretty printing of a bvar object</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>print.bayesianVARs_bvar.Rd</code></div>
     </div>
 
@@ -57,26 +42,26 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_bvar</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_bvar'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>Object of class <code>bayesianVARs_bvar</code>, usually resulting from a call
 of <code><a href="bvar.html">bvar()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Ignored.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns <code>x</code> invisibly.</p>
+    <p>Returns <code>x</code> invisibly.</p>
     </div>
 
     <div class="section level2">
@@ -102,7 +87,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -111,14 +96,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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-  
 
-  
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diff --git a/reference/print.bayesianVARs_predict.html b/reference/print.bayesianVARs_predict.html
index c3c572e..494eaaa 100644
--- a/reference/print.bayesianVARs_predict.html
+++ b/reference/print.bayesianVARs_predict.html
@@ -1,53 +1,38 @@
 <!DOCTYPE html>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Print method for bayesianVARs_predict objects</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>print.bayesianVARs_predict.Rd</code></div>
     </div>
 
@@ -57,26 +42,26 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_predict</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_predict'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A <code>bayesianVARs_predict</code> object obtained via
 <code><a href="predict.bayesianVARs_bvar.html">predict.bayesianVARs_bvar()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored!</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns <code>x</code> invisibly.</p>
+    <p>Returns <code>x</code> invisibly.</p>
     </div>
 
     <div class="section level2">
@@ -96,7 +81,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           draws, that do not fulfill the stable criterion.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  595 stable posterior draws remaining for prediction!</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  723 stable posterior draws remaining for prediction!</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">predictions</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Generic functions for bayesianVARs_predict objects:</span>
@@ -105,7 +90,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span>   - plot.bayesianVARs_predict() (alias for pairs.bayesianVARs_predict()).</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -114,14 +99,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/print.summary.bayesianVARs_bvar.html b/reference/print.summary.bayesianVARs_bvar.html
index 87b008a..70768dc 100644
--- a/reference/print.summary.bayesianVARs_bvar.html
+++ b/reference/print.summary.bayesianVARs_bvar.html
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       <img src="../logo.png" class="logo" alt=""><h1>Print method for summary.bayesianVARs_bvar objects</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>print.summary.bayesianVARs_bvar.Rd</code></div>
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@@ -57,26 +42,26 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for summary.bayesianVARs_bvar</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'summary.bayesianVARs_bvar'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A <code>summary.bayesianVARs_bvar</code> object obtained via
 <code><a href="summary.bayesianVARs_bvar.html">summary.bayesianVARs_bvar()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored!</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns <code>x</code> invisibly!</p>
+    <p>Returns <code>x</code> invisibly!</p>
     </div>
 
     <div class="section level2">
@@ -92,20 +77,20 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Posterior median of reduced-form coefficients:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                 GDPC1 CPIAUCSL   FEDFUNDS</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> GDPC1.l1     0.226152  0.00604  1.362e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> CPIAUCSL.l1 -0.063468  0.62045 -4.773e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FEDFUNDS.l1  0.009492  0.03717  1.001e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> intercept    0.005959  0.00120 -4.555e-05</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> GDPC1.l1     0.212473 0.006487  1.697e-02</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> CPIAUCSL.l1 -0.032907 0.607315 -2.100e-03</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> FEDFUNDS.l1  0.004907 0.037169  1.001e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> intercept    0.006025 0.001392 -5.789e-05</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Posterior interquartile range of of reduced-form coefficients:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                GDPC1  CPIAUCSL  FEDFUNDS</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> GDPC1.l1    0.105445 0.0271618 0.0208810</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> CPIAUCSL.l1 0.118059 0.0930382 0.0131568</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FEDFUNDS.l1 0.019176 0.0145773 0.0065391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> intercept   0.001173 0.0007342 0.0001668</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               GDPC1  CPIAUCSL  FEDFUNDS</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> GDPC1.l1    0.15327 0.0289077 0.0267194</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> CPIAUCSL.l1 0.09095 0.0971672 0.0140055</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> FEDFUNDS.l1 0.01419 0.0148861 0.0075846</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> intercept   0.00157 0.0007101 0.0001965</span>
 </code></pre></div>
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@@ -114,14 +99,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/print.summary.bayesianVARs_predict.html b/reference/print.summary.bayesianVARs_predict.html
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       <img src="../logo.png" class="logo" alt=""><h1>Print method for summary.bayesianVARs_predict objects</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>print.summary.bayesianVARs_predict.Rd</code></div>
     </div>
 
@@ -57,25 +42,25 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for summary.bayesianVARs_predict</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'summary.bayesianVARs_predict'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A <code>summary.bayesianVARs_predict</code> object obtained via <code><a href="summary.bayesianVARs_predict.html">summary.bayesianVARs_predict()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns <code>x</code> invisibly.</p>
+    <p>Returns <code>x</code> invisibly.</p>
     </div>
 
     <div class="section level2">
@@ -95,7 +80,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           draws, that do not fulfill the stable criterion.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  586 stable posterior draws remaining for prediction!</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  596 stable posterior draws remaining for prediction!</span>
 <span class="r-in"><span><span class="va">sum</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">predictions</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">sum</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
@@ -103,28 +88,28 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , GDPC1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          t+1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.06812</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.01798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.02909</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.07121</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.01811</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.02637</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , CPIAUCSL</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           t+1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.018438</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.008339</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.003312</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.021118</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.007914</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.003595</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , FEDFUNDS</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           t+1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.021399</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.004006</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.009963</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.020831</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.004230</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.008696</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -133,14 +118,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/specify_prior_phi.html b/reference/specify_prior_phi.html
index 4be8b94..1719230 100644
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+++ b/reference/specify_prior_phi.html
@@ -1,53 +1,38 @@
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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       </ul></div>
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Specify prior on PHI</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>specify_prior_phi.Rd</code></div>
     </div>
 
@@ -87,27 +72,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
+
+
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>Optional. Data matrix (can be a time series object). Each of
 \(M\) columns is assumed to contain a single time-series of length
 \(T\).</p></dd>
 
 
-<dt>M</dt>
+<dt id="arg-m">M<a class="anchor" aria-label="anchor" href="#arg-m"></a></dt>
 <dd><p>positive integer indicating the number of time-series of the VAR.</p></dd>
 
 
-<dt>lags</dt>
+<dt id="arg-lags">lags<a class="anchor" aria-label="anchor" href="#arg-lags"></a></dt>
 <dd><p>positive integer indicating the order of the VAR, i.e. the number
 of lags of the dependent variables included as predictors.</p></dd>
 
 
-<dt>prior</dt>
+<dt id="arg-prior">prior<a class="anchor" aria-label="anchor" href="#arg-prior"></a></dt>
 <dd><p>character, one of <code>"HS"</code>, <code>"R2D2"</code>, <code>"NG"</code>,
 <code>"DL"</code>, <code>"SSVS"</code>, <code>"HMP"</code> or <code>"normal"</code>.</p></dd>
 
 
-<dt>priormean</dt>
+<dt id="arg-priormean">priormean<a class="anchor" aria-label="anchor" href="#arg-priormean"></a></dt>
 <dd><p>real numbers indicating the prior means of the VAR
 coefficients. One single number means that the prior mean of all own-lag
 coefficients w.r.t. the first lag equals <code>priormean</code> and <code>0</code> else.
@@ -118,13 +105,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 VAR coefficients of the respective VAR equations.</p></dd>
 
 
-<dt>PHI_tol</dt>
+<dt id="arg-phi-tol">PHI_tol<a class="anchor" aria-label="anchor" href="#arg-phi-tol"></a></dt>
 <dd><p>Minimum number that the absolute value of a VAR coefficient
 draw can take. Prevents numerical issues that can appear when strong
 shrinkage is enforced if chosen to be greater than zero.</p></dd>
 
 
-<dt>DL_a</dt>
+<dt id="arg-dl-a">DL_a<a class="anchor" aria-label="anchor" href="#arg-dl-a"></a></dt>
 <dd><p>(Single) positive real number. The value is interpreted as the
 concentration parameter for the local scales. Smaller values enforce heavier
 shrinkage. If the argument <code>global_grouping</code> specifies e.g. <code>k</code>
@@ -136,7 +123,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>prior="DL"</code>.</p></dd>
 
 
-<dt>DL_tol</dt>
+<dt id="arg-dl-tol">DL_tol<a class="anchor" aria-label="anchor" href="#arg-dl-tol"></a></dt>
 <dd><p>Minimum number that a parameter draw of one of the shrinking
 parameters of the Dirichlet Laplace prior can take. Prevents numerical
 issues that can appear when strong shrinkage is enforced if chosen to be
@@ -144,7 +131,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>prior="DL"</code>.</p></dd>
 
 
-<dt>R2D2_a</dt>
+<dt id="arg-r-d-a">R2D2_a<a class="anchor" aria-label="anchor" href="#arg-r-d-a"></a></dt>
 <dd><p>(Single) positive real number. The value is interpreted as the
 concentration parameter for the local scales. Smaller values enforce heavier
 shrinkage. If the argument <code>global_grouping</code> specifies e.g. <code>k</code>
@@ -156,7 +143,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>prior="R2D2"</code>.</p></dd>
 
 
-<dt>R2D2_b</dt>
+<dt id="arg-r-d-b">R2D2_b<a class="anchor" aria-label="anchor" href="#arg-r-d-b"></a></dt>
 <dd><p>(Single) positive real number. The value indicates the shape
 parameter of the inverse gamma prior on the (semi-)global scales. If the
 argument <code>global_grouping</code> specifies e.g. <code>k</code> groups, then
@@ -165,14 +152,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 specified if <code>prior="R2D2"</code>.</p></dd>
 
 
-<dt>R2D2_tol</dt>
+<dt id="arg-r-d-tol">R2D2_tol<a class="anchor" aria-label="anchor" href="#arg-r-d-tol"></a></dt>
 <dd><p>Minimum number that a parameter draw of one of the shrinking
 parameters of the R2D2 prior can take. Prevents numerical issues that can
 appear when strong shrinkage is enforced if chosen to be greater than zero.
 <code>R2D2_tol</code> has only to be specified if <code>prior="R2D2"</code>.</p></dd>
 
 
-<dt>NG_a</dt>
+<dt id="arg-ng-a">NG_a<a class="anchor" aria-label="anchor" href="#arg-ng-a"></a></dt>
 <dd><p>(Single) positive real number. The value is interpreted as the
 concentration parameter for the local scales. Smaller values enforce heavier
 shrinkage. If the argument <code>global_grouping</code> specifies e.g. <code>k</code>
@@ -184,7 +171,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>prior="NG"</code>.</p></dd>
 
 
-<dt>NG_b</dt>
+<dt id="arg-ng-b">NG_b<a class="anchor" aria-label="anchor" href="#arg-ng-b"></a></dt>
 <dd><p>(Single) positive real number. The value indicates the shape
 parameter of the inverse gamma prior on the (semi-)global scales. If the
 argument <code>global_grouping</code> specifies e.g. <code>k</code> groups, then
@@ -193,7 +180,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 specified if <code>prior="NG"</code>.</p></dd>
 
 
-<dt>NG_c</dt>
+<dt id="arg-ng-c">NG_c<a class="anchor" aria-label="anchor" href="#arg-ng-c"></a></dt>
 <dd><p>(Single) positive real number. The value indicates the scale
 parameter of the inverse gamma prior on the (semi-)global scales. If the
 argument <code>global_grouping</code> specifies e.g. <code>k</code> groups, then
@@ -205,34 +192,34 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>NG_c</code> has only to be specified if <code>prior="NG"</code>.</p></dd>
 
 
-<dt>NG_tol</dt>
+<dt id="arg-ng-tol">NG_tol<a class="anchor" aria-label="anchor" href="#arg-ng-tol"></a></dt>
 <dd><p>Minimum number that a parameter draw of one of the shrinking
 parameters of the normal-gamma prior can take. Prevents numerical issues
 that can appear when strong shrinkage is enforced if chosen to be greater
 than zero. <code>NG_tol</code> has only to be specified if <code>prior="NG"</code>.</p></dd>
 
 
-<dt>SSVS_c0</dt>
+<dt id="arg-ssvs-c-">SSVS_c0<a class="anchor" aria-label="anchor" href="#arg-ssvs-c-"></a></dt>
 <dd><p>single positive number indicating the (unscaled) standard
 deviation of the spike component. <code>SSVS_c0</code> has only to be specified if
 <code>prior="SSVS"</code>. It should be that \(SSVS_{c0} \ll SSVS_{c1}\)!
 <code>SSVS_c0</code> has only to be specified if <code>prior="SSVS"</code>.</p></dd>
 
 
-<dt>SSVS_c1</dt>
+<dt id="arg-ssvs-c-">SSVS_c1<a class="anchor" aria-label="anchor" href="#arg-ssvs-c-"></a></dt>
 <dd><p>single positive number indicating the (unscaled) standard
 deviation of the slab component. <code>SSVS_c0</code> has only to be specified if
 <code>prior="SSVS"</code>. It should be that \(SSVS_{c0} \ll SSVS_{c1}\)!</p></dd>
 
 
-<dt>SSVS_semiautomatic</dt>
+<dt id="arg-ssvs-semiautomatic">SSVS_semiautomatic<a class="anchor" aria-label="anchor" href="#arg-ssvs-semiautomatic"></a></dt>
 <dd><p>logical. If <code>SSVS_semiautomatic=TRUE</code> both
 <code>SSVS_c0</code> and <code>SSVS_c1</code> will be scaled by the variances of the
 posterior of PHI under a FLAT conjugate (dependent Normal-Wishart prior).
 <code>SSVS_semiautomatic</code> has only to be specified if <code>prior="SSVS"</code>.</p></dd>
 
 
-<dt>SSVS_p</dt>
+<dt id="arg-ssvs-p">SSVS_p<a class="anchor" aria-label="anchor" href="#arg-ssvs-p"></a></dt>
 <dd><p>Either a single positive number in the range <code>(0,1)</code>
 indicating the (fixed) prior inclusion probability of each coefficient. Or
 numeric vector of length 2 with positive entries indicating the shape
@@ -241,21 +228,21 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 specified if <code>prior="SSVS"</code>.</p></dd>
 
 
-<dt>HMP_lambda1</dt>
+<dt id="arg-hmp-lambda-">HMP_lambda1<a class="anchor" aria-label="anchor" href="#arg-hmp-lambda-"></a></dt>
 <dd><p>numeric vector of length 2. Both entries must be positive.
 The first indicates the shape and the second the rate of the Gamma
 hyperprior on own-lag coefficients. <code>HMP_lambda1</code> has only to be
 specified if <code>prior="HMP"</code>.</p></dd>
 
 
-<dt>HMP_lambda2</dt>
+<dt id="arg-hmp-lambda-">HMP_lambda2<a class="anchor" aria-label="anchor" href="#arg-hmp-lambda-"></a></dt>
 <dd><p>numeric vector of length 2. Both entries must be positive.
 The first indicates the shape and the second the rate of the Gamma
 hyperprior on cross-lag coefficients. <code>HMP_lambda2</code> has only to be
 specified if <code>prior="HMP"</code>.</p></dd>
 
 
-<dt>normal_sds</dt>
+<dt id="arg-normal-sds">normal_sds<a class="anchor" aria-label="anchor" href="#arg-normal-sds"></a></dt>
 <dd><p>numeric vector of length \(n\), where \(n = lags M^2\) is
 the number of all VAR coefficients (excluding the intercept), indicating the
 prior variances. A single number will be recycled accordingly! Must be
@@ -263,7 +250,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>prior="normal"</code>.</p></dd>
 
 
-<dt>global_grouping</dt>
+<dt id="arg-global-grouping">global_grouping<a class="anchor" aria-label="anchor" href="#arg-global-grouping"></a></dt>
 <dd><p>One of <code>"global"</code>, <code>"equation-wise"</code>,
 <code>"covariate-wise"</code>, <code>"olcl-lagwise"</code> <code>"fol"</code> indicating the
 sub-groups of the semi-global(-local) modifications to HS, R2D2, NG, DL and
@@ -272,15 +259,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>prior="R2D2"</code>, <code>prior="NG"</code> or <code>prior="SSVS"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Do not use!</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A <code>baysianVARs_prior_phi</code>-object.</p>
+    <p>A <code>baysianVARs_prior_phi</code>-object.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -331,7 +316,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -340,14 +325,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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index 2e13b02..6ec5e26 100644
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@@ -1,53 +1,38 @@
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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Specify prior on Sigma</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>specify_prior_sigma.Rd</code></div>
     </div>
 
@@ -107,27 +92,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
+
+
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>Optional. Data matrix (can be a time series object). Each of
 \(M\) columns is assumed to contain a single time-series of length
 \(T\).</p></dd>
 
 
-<dt>M</dt>
+<dt id="arg-m">M<a class="anchor" aria-label="anchor" href="#arg-m"></a></dt>
 <dd><p>positive integer indicating the number of time-series of the VAR.</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p>character, one of <code>"factor"</code> (the default) or <code>"cholesky"</code>,
 indicating which decomposition to be applied to the covariance-matrix.</p></dd>
 
 
-<dt>factor_factors</dt>
+<dt id="arg-factor-factors">factor_factors<a class="anchor" aria-label="anchor" href="#arg-factor-factors"></a></dt>
 <dd><p>Number of latent factors to be estimated. Only required
 if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_restrict</dt>
+<dt id="arg-factor-restrict">factor_restrict<a class="anchor" aria-label="anchor" href="#arg-factor-restrict"></a></dt>
 <dd><p>Either "upper" or "none", indicating whether the factor
 loadings matrix should be restricted to have zeros above the diagonal
 ("upper") or whether all elements should be estimated from the data
@@ -139,7 +126,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 is the goal. Only required if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_priorfacloadtype</dt>
+<dt id="arg-factor-priorfacloadtype">factor_priorfacloadtype<a class="anchor" aria-label="anchor" href="#arg-factor-priorfacloadtype"></a></dt>
 <dd><p>Can be <code>"normal"</code>, <code>"rowwiseng"</code>,
 <code>"colwiseng"</code>. Only required if <code>type="factor"</code>.</p><dl><dt><code>"normal"</code>: </dt>
 <dd><p>Normal prior. The value of <code>priorfacload</code>
@@ -158,7 +145,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl><p>For details please see Kastner (2019).</p></dd>
 
 
-<dt>factor_priorfacload</dt>
+<dt id="arg-factor-priorfacload">factor_priorfacload<a class="anchor" aria-label="anchor" href="#arg-factor-priorfacload"></a></dt>
 <dd><p>Either a matrix of dimensions <code>M</code> times
 <code>factor_factors</code> with positive elements or a single number (which will
 be recycled accordingly). Only required if <code>type="factor"</code>. The meaning of
@@ -166,54 +153,54 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>factor_priorfacloadtype</code> and is explained there.</p></dd>
 
 
-<dt>factor_facloadtol</dt>
+<dt id="arg-factor-facloadtol">factor_facloadtol<a class="anchor" aria-label="anchor" href="#arg-factor-facloadtol"></a></dt>
 <dd><p>Minimum number that the absolute value of a factor
 loadings draw can take. Prevents numerical issues that can appear when
 strong shrinkage is enforced if chosen to be greater than zero. Only
 required if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_priorng</dt>
+<dt id="arg-factor-priorng">factor_priorng<a class="anchor" aria-label="anchor" href="#arg-factor-priorng"></a></dt>
 <dd><p>Two-element vector with positive entries indicating the
 Normal-Gamma prior's hyperhyperparameters <code>c</code> and <code>d</code> (cf. Kastner
 (2019)). Only required if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_priormu</dt>
+<dt id="arg-factor-priormu">factor_priormu<a class="anchor" aria-label="anchor" href="#arg-factor-priormu"></a></dt>
 <dd><p>Vector of length 2 denoting prior mean and standard
 deviation for unconditional levels of the idiosyncratic log variance
 processes. Only required if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_priorphiidi</dt>
+<dt id="arg-factor-priorphiidi">factor_priorphiidi<a class="anchor" aria-label="anchor" href="#arg-factor-priorphiidi"></a></dt>
 <dd><p>Vector of length 2, indicating the shape parameters
 for the Beta prior distributions of the transformed parameters
 <code>(phi+1)/2</code>, where <code>phi</code> denotes the persistence of the
 idiosyncratic log variances. Only required if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_priorphifac</dt>
+<dt id="arg-factor-priorphifac">factor_priorphifac<a class="anchor" aria-label="anchor" href="#arg-factor-priorphifac"></a></dt>
 <dd><p>Vector of length 2, indicating the shape parameters
 for the Beta prior distributions of the transformed parameters
 <code>(phi+1)/2</code>, where <code>phi</code> denotes the persistence of the factor log
 variances. Only required if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_priorsigmaidi</dt>
+<dt id="arg-factor-priorsigmaidi">factor_priorsigmaidi<a class="anchor" aria-label="anchor" href="#arg-factor-priorsigmaidi"></a></dt>
 <dd><p>Vector of length <code>M</code> containing the prior
 volatilities of log variances. If <code>factor_priorsigmaidi</code> has exactly
 one element, it will be recycled for all idiosyncratic log variances. Only
 required if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_priorsigmafac</dt>
+<dt id="arg-factor-priorsigmafac">factor_priorsigmafac<a class="anchor" aria-label="anchor" href="#arg-factor-priorsigmafac"></a></dt>
 <dd><p>Vector of length <code>factor_factors</code> containing
 the prior volatilities of log variances. If <code>factor_priorsigmafac</code> has
 exactly one element, it will be recycled for all factor log variances. Only
 required if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_priorh0idi</dt>
+<dt id="arg-factor-priorh-idi">factor_priorh0idi<a class="anchor" aria-label="anchor" href="#arg-factor-priorh-idi"></a></dt>
 <dd><p>Vector of length 1 or <code>M</code>, containing
 information about the Gaussian prior for the initial idiosyncratic log
 variances. Only required if <code>type="factor"</code>. If an element of
@@ -226,7 +213,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 variance \(sigma^2/(1-phi^2)\).</p></dd>
 
 
-<dt>factor_priorh0fac</dt>
+<dt id="arg-factor-priorh-fac">factor_priorh0fac<a class="anchor" aria-label="anchor" href="#arg-factor-priorh-fac"></a></dt>
 <dd><p>Vector of length 1 or <code>factor_factors</code>,
 containing information about the Gaussian prior for the initial factor log
 variances. Only required if <code>type="factor"</code>. If an element of
@@ -239,7 +226,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 variance \(sigma^2/(1-phi^2)\).</p></dd>
 
 
-<dt>factor_heteroskedastic</dt>
+<dt id="arg-factor-heteroskedastic">factor_heteroskedastic<a class="anchor" aria-label="anchor" href="#arg-factor-heteroskedastic"></a></dt>
 <dd><p>Vector of length 1, 2, or <code>M +
  factor_factors</code>, containing logical values indicating whether time-varying
 (<code>factor_heteroskedastic = TRUE</code>) or constant (<code>factor_heteroskedastic =
@@ -249,7 +236,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 factor variances. Only required if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_priorhomoskedastic</dt>
+<dt id="arg-factor-priorhomoskedastic">factor_priorhomoskedastic<a class="anchor" aria-label="anchor" href="#arg-factor-priorhomoskedastic"></a></dt>
 <dd><p>Only used if at least one element of
 <code>factor_heteroskedastic</code> is set to <code>FALSE</code>. In that case,
 <code>factor_priorhomoskedastic</code> must be a matrix with positive entries and
@@ -259,7 +246,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 variances. Only required if <code>type="factor"</code>.</p></dd>
 
 
-<dt>factor_interweaving</dt>
+<dt id="arg-factor-interweaving">factor_interweaving<a class="anchor" aria-label="anchor" href="#arg-factor-interweaving"></a></dt>
 <dd><p>The following values for interweaving the factor
 loadings are accepted (Only required if <code>type="factor"</code>):</p><dl><dt>0: </dt>
 <dd><p>No interweaving.</p></dd>
@@ -281,47 +268,47 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 recommended default.</p></dd>
 
 
-<dt>cholesky_U_prior</dt>
+<dt id="arg-cholesky-u-prior">cholesky_U_prior<a class="anchor" aria-label="anchor" href="#arg-cholesky-u-prior"></a></dt>
 <dd><p>character, one of <code>"HS"</code>, <code>"R2D2"</code>, <code>"NG"</code>,
 <code>"DL"</code>, <code>"SSVS"</code>, <code>"HMP"</code> or <code>"normal"</code>. Only required
 if <code>type="cholesky"</code>.</p></dd>
 
 
-<dt>cholesky_U_tol</dt>
+<dt id="arg-cholesky-u-tol">cholesky_U_tol<a class="anchor" aria-label="anchor" href="#arg-cholesky-u-tol"></a></dt>
 <dd><p>Minimum number that the absolute value of an free
 off-diagonal element of an \(U\)-draw can take. Prevents numerical issues
 that can appear when strong shrinkage is enforced if chosen to be greater
 than zero. Only required if <code>type="cholesky"</code>.</p></dd>
 
 
-<dt>cholesky_heteroscedastic</dt>
+<dt id="arg-cholesky-heteroscedastic">cholesky_heteroscedastic<a class="anchor" aria-label="anchor" href="#arg-cholesky-heteroscedastic"></a></dt>
 <dd><p>single logical indicating whether time-varying
 (<code>cholesky_heteroscedastic = TRUE</code>) or constant
 (<code>cholesky_heteroscedastic = FALSE</code>) variance should be estimated. Only
 required if <code>type="cholesky"</code>.</p></dd>
 
 
-<dt>cholesky_priormu</dt>
+<dt id="arg-cholesky-priormu">cholesky_priormu<a class="anchor" aria-label="anchor" href="#arg-cholesky-priormu"></a></dt>
 <dd><p>Vector of length 2 denoting prior mean and standard
 deviation for unconditional levels of the log variance processes. Only
 required if <code>type="cholesky"</code>.</p></dd>
 
 
-<dt>cholesky_priorphi</dt>
+<dt id="arg-cholesky-priorphi">cholesky_priorphi<a class="anchor" aria-label="anchor" href="#arg-cholesky-priorphi"></a></dt>
 <dd><p>Vector of length 2, indicating the shape parameters
 for the Beta prior distributions of the transformed parameters
 <code>(phi+1)/2</code>, where <code>phi</code> denotes the persistence of the log
 variances. Only required if <code>type="cholesky"</code>.</p></dd>
 
 
-<dt>cholesky_priorsigma2</dt>
+<dt id="arg-cholesky-priorsigma-">cholesky_priorsigma2<a class="anchor" aria-label="anchor" href="#arg-cholesky-priorsigma-"></a></dt>
 <dd><p>Vector of length 2, indicating the shape and the
 rate for the Gamma prior distributions on the variance of the log variance
 processes. (Currently only one global setting for all \(M\) processes is
 supported). Only required if <code>type="cholesky"</code>.</p></dd>
 
 
-<dt>cholesky_priorh0</dt>
+<dt id="arg-cholesky-priorh-">cholesky_priorh0<a class="anchor" aria-label="anchor" href="#arg-cholesky-priorh-"></a></dt>
 <dd><p>Vector of length 1 or <code>M</code>, containing information
 about the Gaussian prior for the initial idiosyncratic log variances. Only
 required if <code>type="cholesky"</code>. If an element of <code>cholesky_priorh0</code> is a
@@ -334,7 +321,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 \(sigma^2/(1-phi^2)\).</p></dd>
 
 
-<dt>cholesky_priorhomoscedastic</dt>
+<dt id="arg-cholesky-priorhomoscedastic">cholesky_priorhomoscedastic<a class="anchor" aria-label="anchor" href="#arg-cholesky-priorhomoscedastic"></a></dt>
 <dd><p>Only used if
 <code>cholesky_heteroscedastic=FALSE</code>. In that case,
 <code>cholesky_priorhomoscedastic</code> must be a matrix with positive entries
@@ -344,7 +331,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 required if <code>type="cholesky"</code>.</p></dd>
 
 
-<dt>cholesky_DL_a</dt>
+<dt id="arg-cholesky-dl-a">cholesky_DL_a<a class="anchor" aria-label="anchor" href="#arg-cholesky-dl-a"></a></dt>
 <dd><p>(Single) positive real number. The value is interpreted
 as the concentration parameter for the local scales. Smaller values enforce
 heavier shrinkage. A matrix of dimension <code>c(s,2)</code> specifies a discrete
@@ -353,7 +340,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 to be specified if <code>cholesky_U_prior="DL"</code>.</p></dd>
 
 
-<dt>cholesky_DL_tol</dt>
+<dt id="arg-cholesky-dl-tol">cholesky_DL_tol<a class="anchor" aria-label="anchor" href="#arg-cholesky-dl-tol"></a></dt>
 <dd><p>Minimum number that a parameter draw of one of the
 shrinking parameters of the Dirichlet Laplace prior can take. Prevents
 numerical issues that can appear when strong shrinkage is enforced if chosen
@@ -361,7 +348,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>cholesky_U_prior="DL"</code>.</p></dd>
 
 
-<dt>cholesky_R2D2_a</dt>
+<dt id="arg-cholesky-r-d-a">cholesky_R2D2_a<a class="anchor" aria-label="anchor" href="#arg-cholesky-r-d-a"></a></dt>
 <dd><p>(Single) positive real number. The value is interpreted
 as the concentration parameter for the local scales. Smaller values enforce
 heavier shrinkage. A matrix of dimension <code>c(s,2)</code> specifies a discrete
@@ -370,13 +357,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 to be specified if <code>cholesky_U_prior="R2D2"</code>.</p></dd>
 
 
-<dt>cholesky_R2D2_b</dt>
+<dt id="arg-cholesky-r-d-b">cholesky_R2D2_b<a class="anchor" aria-label="anchor" href="#arg-cholesky-r-d-b"></a></dt>
 <dd><p>single positive number, where greater values indicate
 heavier regularization. <code>cholesky_R2D2_b</code> has only to be specified if
 <code>cholesky_U_prior="R2D2"</code>.</p></dd>
 
 
-<dt>cholesky_R2D2_tol</dt>
+<dt id="arg-cholesky-r-d-tol">cholesky_R2D2_tol<a class="anchor" aria-label="anchor" href="#arg-cholesky-r-d-tol"></a></dt>
 <dd><p>Minimum number that a parameter draw of one of the
 shrinking parameters of the R2D2 prior can take. Prevents numerical issues
 that can appear when strong shrinkage is enforced if chosen to be greater
@@ -384,7 +371,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>cholesky_U_prior="R2D2"</code>.</p></dd>
 
 
-<dt>cholesky_NG_a</dt>
+<dt id="arg-cholesky-ng-a">cholesky_NG_a<a class="anchor" aria-label="anchor" href="#arg-cholesky-ng-a"></a></dt>
 <dd><p>(Single) positive real number. The value is interpreted
 as the concentration parameter for the local scales. Smaller values enforce
 heavier shrinkage. A matrix of dimension <code>c(s,2)</code> specifies a discrete
@@ -393,13 +380,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 to be specified if <code>cholesky_U_prior="NG"</code>.</p></dd>
 
 
-<dt>cholesky_NG_b</dt>
+<dt id="arg-cholesky-ng-b">cholesky_NG_b<a class="anchor" aria-label="anchor" href="#arg-cholesky-ng-b"></a></dt>
 <dd><p>(Single) positive real number. The value indicates the
 shape parameter of the inverse gamma prior on the global scales.
 <code>cholesky_NG_b</code> has only to be specified if <code>cholesky_U_prior="NG"</code>.</p></dd>
 
 
-<dt>cholesky_NG_c</dt>
+<dt id="arg-cholesky-ng-c">cholesky_NG_c<a class="anchor" aria-label="anchor" href="#arg-cholesky-ng-c"></a></dt>
 <dd><p>(Single) positive real number. The value indicates the
 scale parameter of the inverse gamma prior on the global scales.
 Expert option would be to set the scale parameter proportional to NG_a. E.g.
@@ -409,7 +396,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>cholesky_U_prior="NG"</code>.</p></dd>
 
 
-<dt>cholesky_NG_tol</dt>
+<dt id="arg-cholesky-ng-tol">cholesky_NG_tol<a class="anchor" aria-label="anchor" href="#arg-cholesky-ng-tol"></a></dt>
 <dd><p>Minimum number that a parameter draw of one of the
 shrinking parameters of the normal-gamma prior can take. Prevents numerical
 issues that can appear when strong shrinkage is enforced if chosen to be
@@ -417,7 +404,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>cholesky_U_prior="NG"</code>.</p></dd>
 
 
-<dt>cholesky_SSVS_c0</dt>
+<dt id="arg-cholesky-ssvs-c-">cholesky_SSVS_c0<a class="anchor" aria-label="anchor" href="#arg-cholesky-ssvs-c-"></a></dt>
 <dd><p>single positive number indicating the (unscaled)
 standard deviation of the spike component. <code>cholesky_SSVS_c0</code> has only
 to be specified if <code>choleksy_U_prior="SSVS"</code>.
@@ -425,14 +412,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
   \ll SSVS_{c1}\)!</p></dd>
 
 
-<dt>cholesky_SSVS_c1</dt>
+<dt id="arg-cholesky-ssvs-c-">cholesky_SSVS_c1<a class="anchor" aria-label="anchor" href="#arg-cholesky-ssvs-c-"></a></dt>
 <dd><p>single positive number indicating the (unscaled)
 standard deviation of the slab component. <code>cholesky_SSVS_c1</code> has only
 to be specified if <code>choleksy_U_prior="SSVS"</code>. It should be that
 \(SSVS_{c0} \ll SSVS_{c1}\)!</p></dd>
 
 
-<dt>cholesky_SSVS_p</dt>
+<dt id="arg-cholesky-ssvs-p">cholesky_SSVS_p<a class="anchor" aria-label="anchor" href="#arg-cholesky-ssvs-p"></a></dt>
 <dd><p>Either a single positive number in the range <code>(0,1)</code>
 indicating the (fixed) prior inclusion probability of each coefficient. Or
 numeric vector of length 2 with positive entries indicating the shape
@@ -441,14 +428,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 specified if <code>choleksy_U_prior="SSVS"</code>.</p></dd>
 
 
-<dt>cholesky_HMP_lambda3</dt>
+<dt id="arg-cholesky-hmp-lambda-">cholesky_HMP_lambda3<a class="anchor" aria-label="anchor" href="#arg-cholesky-hmp-lambda-"></a></dt>
 <dd><p>numeric vector of length 2. Both entries must be
 positive. The first indicates the shape and the second the rate of the Gamma
 hyperprior on the contemporaneous coefficients. <code>cholesky_HMP_lambda3</code> has
 only to be specified if <code>choleksy_U_prior="HMP"</code>.</p></dd>
 
 
-<dt>cholesky_normal_sds</dt>
+<dt id="arg-cholesky-normal-sds">cholesky_normal_sds<a class="anchor" aria-label="anchor" href="#arg-cholesky-normal-sds"></a></dt>
 <dd><p>numeric vector of length \(\frac{M^2-M}{2}\),
 indicating the prior variances for the free off-diagonal elements in
 \(U\). A single number will be recycled accordingly! Must be positive.
@@ -456,23 +443,21 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>choleksy_U_prior="normal"</code>.</p></dd>
 
 
-<dt>expert_sv_offset</dt>
+<dt id="arg-expert-sv-offset">expert_sv_offset<a class="anchor" aria-label="anchor" href="#arg-expert-sv-offset"></a></dt>
 <dd><p>... Do not use!</p></dd>
 
 
-<dt>quiet</dt>
+<dt id="arg-quiet">quiet<a class="anchor" aria-label="anchor" href="#arg-quiet"></a></dt>
 <dd><p>logical indicating whether informative output should be omitted.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Do not use!</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Object of class <code>bayesianVARs_prior_sigma</code>.</p>
+    <p>Object of class <code>bayesianVARs_prior_sigma</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -502,11 +487,11 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Kastner, G. (2019). Sparse Bayesian Time-Varying Covariance
 Estimation in Many Dimensions <em>Journal of Econometrics</em>, <b>210</b>(1),
-98--115, <a href="https://doi.org/10.1016/j.jeconom.2018.11.007" class="external-link">doi:10.1016/j.jeconom.2018.11.007</a></p>
+98–115, <a href="https://doi.org/10.1016/j.jeconom.2018.11.007" class="external-link">doi:10.1016/j.jeconom.2018.11.007</a></p>
 <p>Kastner, G., Frühwirth-Schnatter, S., and Lopes, H.F. (2017).
 Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility
 Models. <em>Journal of Computational and Graphical Statistics</em>,
-<b>26</b>(4), 905--917, <a href="https://doi.org/10.1080/10618600.2017.1322091" class="external-link">doi:10.1080/10618600.2017.1322091</a>
+<b>26</b>(4), 905–917, <a href="https://doi.org/10.1080/10618600.2017.1322091" class="external-link">doi:10.1080/10618600.2017.1322091</a>
 .</p>
     </div>
     <div class="section level2">
@@ -557,7 +542,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -566,14 +551,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/stable_bvar.html b/reference/stable_bvar.html
index 8e1ea38..aa6e58b 100644
--- a/reference/stable_bvar.html
+++ b/reference/stable_bvar.html
@@ -1,59 +1,44 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="stable_bvar() detects and discards all posterior draws of an
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Stable posterior draws — stable_bvar • bayesianVARs</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Stable posterior draws — stable_bvar"><meta name="description" content="stable_bvar() detects and discards all posterior draws of an
 bayesianVARs_bvar object that do not fulfill the stability condition:
 A VAR(p) model is considered as stable only if the eigenvalues of the
-companion form matrix lie inside the unit circle."><title>Stable posterior draws — stable_bvar • bayesianVARs</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Stable posterior draws — stable_bvar"><meta property="og:description" content="stable_bvar() detects and discards all posterior draws of an
+companion form matrix lie inside the unit circle."><meta property="og:description" content="stable_bvar() detects and discards all posterior draws of an
 bayesianVARs_bvar object that do not fulfill the stability condition:
 A VAR(p) model is considered as stable only if the eigenvalues of the
-companion form matrix lie inside the unit circle."><meta property="og:image" content="https://luisgruber.github.io/bayesianVARs/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+companion form matrix lie inside the unit circle."><meta property="og:image" content="https://luisgruber.github.io/bayesianVARs/logo.png"></head><body>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.3</small>
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       <img src="../logo.png" class="logo" alt=""><h1>Stable posterior draws</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/bvar_wrapper.R" class="external-link"><code>R/bvar_wrapper.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/bvar_wrapper.R" class="external-link"><code>R/bvar_wrapper.R</code></a></small>
       <div class="d-none name"><code>stable_bvar.Rd</code></div>
     </div>
 
@@ -71,19 +56,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A <code>bayesianVARs_bvar</code> object obtained via <code><a href="bvar.html">bvar()</a></code>.</p></dd>
 
 
-<dt>quiet</dt>
+<dt id="arg-quiet">quiet<a class="anchor" aria-label="anchor" href="#arg-quiet"></a></dt>
 <dd><p>logical indicating whether informative output should be omitted.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An object of type <code>bayesianVARs_bvar</code>.</p>
+    <p>An object of type <code>bayesianVARs_bvar</code>.</p>
     </div>
 
     <div class="section level2">
@@ -99,13 +84,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Original 'bayesianVARs_bvar' object consists of 1000 posterior draws.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Detected 475 unstable draws.</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Detected 540 unstable draws.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Remaining draws: 525 !</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Remaining draws: 460 !</span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -114,14 +99,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/reference/sub-.bayesianVARs_coef.html b/reference/sub-.bayesianVARs_coef.html
index b7394db..5f7dcaa 100644
--- a/reference/sub-.bayesianVARs_coef.html
+++ b/reference/sub-.bayesianVARs_coef.html
@@ -1,53 +1,38 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Extract or Replace Parts of a bayesianVARs_coef object</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/bvar_wrapper.R" class="external-link"><code>R/bvar_wrapper.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/bvar_wrapper.R" class="external-link"><code>R/bvar_wrapper.R</code></a></small>
       <div class="d-none name"><code>sub-.bayesianVARs_coef.Rd</code></div>
     </div>
 
@@ -57,33 +42,33 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre><code># S3 method for bayesianVARs_coef
-[(x, i, j, ...)</code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_coef'</span></span>
+<span><span class="va">x</span><span class="op">[</span><span class="va">i</span>, <span class="va">j</span>, <span class="va">...</span><span class="op">]</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object of type <code>bayesianVARs_coef</code>.</p></dd>
 
 
-<dt>i</dt>
+<dt id="arg-i">i<a class="anchor" aria-label="anchor" href="#arg-i"></a></dt>
 <dd><p>indices</p></dd>
 
 
-<dt>j</dt>
+<dt id="arg-j">j<a class="anchor" aria-label="anchor" href="#arg-j"></a></dt>
 <dd><p>indices</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>further indices</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An object of type <code>bayesianVARs_coef</code>.</p>
+    <p>An object of type <code>bayesianVARs_coef</code>.</p>
     </div>
 
     <div class="section level2">
@@ -97,12 +82,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># Extract coefficients, which are of class bayesianVARs_coef</span></span></span>
 <span class="r-in"><span><span class="va">phi</span> <span class="op">&lt;-</span> <span class="fu"><a href="coef.html">coef</a></span><span class="op">(</span><span class="va">mod</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">phi</span><span class="op">[</span><span class="fl">1</span>,<span class="fl">1</span>,<span class="fl">1</span><span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.2644423</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.1591042</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"class")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "bayesianVARs_coef"  "bayesianVARs_draws"</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -111,14 +96,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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diff --git a/reference/sub-.bayesianVARs_draws.html b/reference/sub-.bayesianVARs_draws.html
index 0807671..a02f3f7 100644
--- a/reference/sub-.bayesianVARs_draws.html
+++ b/reference/sub-.bayesianVARs_draws.html
@@ -1,53 +1,38 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Extract or Replace Parts of a bayesianVARs_draws object</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/bvar_wrapper.R" class="external-link"><code>R/bvar_wrapper.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/bvar_wrapper.R" class="external-link"><code>R/bvar_wrapper.R</code></a></small>
       <div class="d-none name"><code>sub-.bayesianVARs_draws.Rd</code></div>
     </div>
 
@@ -57,33 +42,33 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre><code># S3 method for bayesianVARs_draws
-[(x, i, j, ...)</code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_draws'</span></span>
+<span><span class="va">x</span><span class="op">[</span><span class="va">i</span>, <span class="va">j</span>, <span class="va">...</span><span class="op">]</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object of type <code>bayesianVARs_draws</code>.</p></dd>
 
 
-<dt>i</dt>
+<dt id="arg-i">i<a class="anchor" aria-label="anchor" href="#arg-i"></a></dt>
 <dd><p>indices</p></dd>
 
 
-<dt>j</dt>
+<dt id="arg-j">j<a class="anchor" aria-label="anchor" href="#arg-j"></a></dt>
 <dd><p>indices</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>further indices</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An object of type <code>bayesianVARs_draws</code>.</p>
+    <p>An object of type <code>bayesianVARs_draws</code>.</p>
     </div>
 
     <div class="section level2">
@@ -97,12 +82,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># Extract coefficients, which are of class bayesianVARs_draws</span></span></span>
 <span class="r-in"><span><span class="va">phi</span> <span class="op">&lt;-</span> <span class="fu"><a href="coef.html">coef</a></span><span class="op">(</span><span class="va">mod</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">phi</span><span class="op">[</span><span class="fl">1</span>,<span class="fl">1</span>,<span class="fl">1</span><span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.2662653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.3361933</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"class")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "bayesianVARs_coef"  "bayesianVARs_draws"</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -111,14 +96,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/reference/summary.bayesianVARs_bvar.html b/reference/summary.bayesianVARs_bvar.html
index 04bf2c5..a94ba33 100644
--- a/reference/summary.bayesianVARs_bvar.html
+++ b/reference/summary.bayesianVARs_bvar.html
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       <img src="../logo.png" class="logo" alt=""><h1>Summary method for bayesianVARs_bvar objects</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>summary.bayesianVARs_bvar.Rd</code></div>
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@@ -57,29 +42,29 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_bvar</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_bvar'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, quantiles <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.025</span>, <span class="fl">0.25</span>, <span class="fl">0.5</span>, <span class="fl">0.75</span>, <span class="fl">0.975</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A <code>bayesianVARs_bvar</code> object obtained via <code><a href="bvar.html">bvar()</a></code>.</p></dd>
 
 
-<dt>quantiles</dt>
+<dt id="arg-quantiles">quantiles<a class="anchor" aria-label="anchor" href="#arg-quantiles"></a></dt>
 <dd><p>numeric vector which quantiles to compute.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored!</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An object of type <code>summary.bayesianVARs_bvar</code>.</p>
+    <p>An object of type <code>summary.bayesianVARs_bvar</code>.</p>
     </div>
 
     <div class="section level2">
@@ -94,7 +79,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">sum</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mod</span><span class="op">)</span></span></span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -103,14 +88,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/summary.bayesianVARs_draws.html b/reference/summary.bayesianVARs_draws.html
index 0c27868..5a6535d 100644
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+++ b/reference/summary.bayesianVARs_draws.html
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+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Summary statistics for bayesianVARs posterior draws.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>summary.bayesianVARs_draws.Rd</code></div>
     </div>
 
@@ -57,31 +42,31 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_draws</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_draws'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, quantiles <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">0.5</span>, <span class="fl">0.75</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An object of class <code>bayesianVARs_draws</code> usually obtained through
 extractors like <code><a href="coef.html">coef.bayesianVARs_bvar()</a></code> and
 <code><a href="vcov.bayesianVARs_bvar.html">vcov.bayesianVARs_bvar()</a></code>.</p></dd>
 
 
-<dt>quantiles</dt>
+<dt id="arg-quantiles">quantiles<a class="anchor" aria-label="anchor" href="#arg-quantiles"></a></dt>
 <dd><p>A vector of quantiles to evaluate.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list object of class <code>bayesianVARs_draws_summary</code> holding</p><ul><li><p><code>mean</code>: Vector or matrix containing the posterior mean.</p></li>
+    <p>A list object of class <code>bayesianVARs_draws_summary</code> holding</p><ul><li><p><code>mean</code>: Vector or matrix containing the posterior mean.</p></li>
 <li><p><code>sd</code>: Vector or matrix containing the posterior standard deviation .</p></li>
 <li><p><code>quantiles</code>: Array containing the posterior quantiles.</p></li>
 </ul></div>
@@ -114,7 +99,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -123,14 +108,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/summary.bayesianVARs_predict.html b/reference/summary.bayesianVARs_predict.html
index 9e5a592..f38688a 100644
--- a/reference/summary.bayesianVARs_predict.html
+++ b/reference/summary.bayesianVARs_predict.html
@@ -1,53 +1,38 @@
 <!DOCTYPE html>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Summary method for bayesianVARs_predict objects</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>summary.bayesianVARs_predict.Rd</code></div>
     </div>
 
@@ -57,26 +42,26 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_predict</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_predict'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A <code>bayesianVARs_predict</code> object obtained via
 <code><a href="predict.bayesianVARs_bvar.html">predict.bayesianVARs_bvar()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored!</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A <code>summary.bayesianVARs_predict</code> object.</p>
+    <p>A <code>summary.bayesianVARs_predict</code> object.</p>
     </div>
 
     <div class="section level2">
@@ -96,34 +81,34 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           draws, that do not fulfill the stable criterion.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  597 stable posterior draws remaining for prediction!</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  509 stable posterior draws remaining for prediction!</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">predictions</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Prediction quantiles:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , GDPC1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          t+1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.06623</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.02089</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.02220</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.07418</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.02086</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.03223</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , CPIAUCSL</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           t+1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.019613</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.008145</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.003785</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.020029</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.007975</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.003687</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , FEDFUNDS</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           t+1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.022382</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.003965</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.013119</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5%  -0.020075</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50% -0.003383</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 95%  0.016755</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="21 selected quarterly time-series from 1953:Q1 to 2021:Q2. From FRED-QD data
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       <img src="../logo.png" class="logo" alt=""><h1>Data from the US-economy</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/data.R" class="external-link"><code>R/data.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/data.R" class="external-link"><code>R/data.R</code></a></small>
       <div class="d-none name"><code>usmacro_growth.Rd</code></div>
     </div>
 
@@ -83,11 +68,11 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>McCracken, M. W. and Ng, S. (2021). FRED-QD: A Quarterly
 Database for Macroeconomic Research, <em>Review, Federal Reserve Bank of St.
-Louis</em>, <b>103</b>(1), 1--44, <a href="https://doi.org/10.20955/r.103.1-44" class="external-link">doi:10.20955/r.103.1-44</a>
+Louis</em>, <b>103</b>(1), 1–44, <a href="https://doi.org/10.20955/r.103.1-44" class="external-link">doi:10.20955/r.103.1-44</a>
 .</p>
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@@ -1,59 +1,44 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Returns the posterior draws of the possibly time-varying variance-covariance
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       <img src="../logo.png" class="logo" alt=""><h1>Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model</h1>
-      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/HEAD/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/luisgruber/bayesianVARs/blob/main/R/utility_functions.R" class="external-link"><code>R/utility_functions.R</code></a></small>
       <div class="d-none name"><code>vcov.bayesianVARs_bvar.Rd</code></div>
     </div>
 
@@ -66,30 +51,30 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for bayesianVARs_bvar</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'bayesianVARs_bvar'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/vcov.html" class="external-link">vcov</a></span><span class="op">(</span><span class="va">object</span>, t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq_len</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">logvar</span><span class="op">)</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An object of class <code>bayesianVARs_bvar</code> obtained via <code><a href="bvar.html">bvar()</a></code>.</p></dd>
 
 
-<dt>t</dt>
+<dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
 <dd><p>Vector indicating which points in time should be extracted, defaults
 to all.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An array of class <code>bayesianVARs_draws</code> of dimension \(T \times M
+    <p>An array of class <code>bayesianVARs_draws</code> of dimension \(T \times M
   \times M \times draws\), where \(T\) is the number of observations,
 \(M\) the number of time-series and \(draws\) the number of stored
 posterior draws.</p>
@@ -110,7 +95,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">bvar_vcov</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/vcov.html" class="external-link">vcov</a></span><span class="op">(</span><span class="va">mod</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -119,14 +104,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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diff --git a/search.json b/search.json
index 9c6424a..c872856 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
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Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see <http://www.gnu.org/licenses/>. GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.> Copyright (C) <year>  <name of author>  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see <http://www.gnu.org/licenses/>. <program>  Copyright (C) <year>  <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://luisgruber.github.io/bayesianVARs/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Luis Gruber. Copyright holder, author, maintainer. Gregor Kastner. Contributor.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Gruber L (2024). bayesianVARs: MCMC Estimation Bayesian Vectorautoregressions. R package version 0.1.3,  https://luisgruber.github.io/bayesianVARs/, https://github.com/luisgruber/bayesianVARs.","code":"@Manual{,   title = {bayesianVARs: MCMC Estimation of Bayesian Vectorautoregressions},   author = {Luis Gruber},   year = {2024},   note = {R package version 0.1.3,  https://luisgruber.github.io/bayesianVARs/},   url = {https://github.com/luisgruber/bayesianVARs}, }"},{"path":"https://luisgruber.github.io/bayesianVARs/index.html","id":"bayesianvars-","dir":"","previous_headings":"","what":"MCMC Estimation of Bayesian Vectorautoregressions","title":"MCMC Estimation of Bayesian Vectorautoregressions","text":"Estimation Bayesian vectorautoregressions /without stochastic volatility. Implements several modern hierarchical shrinkage priors, amongst Dirichlet-Laplace prior (DL), hierarchical Minnesota prior (HM), Horseshoe prior (HS), normal-gamma prior (NG), R2-induced-Dirichlet-decomposition prior (R2D2) stochastic search variable selection prior (SSVS). Concerning error-term, user can either specify order-invariant factor structure order-variant cholesky structure.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MCMC Estimation of Bayesian Vectorautoregressions","text":"Install CRAN version: Install latest development version directly GitHub:","code":"install.packages(\"bayesianVARs\") devtools::install_github(\"luisgruber/bayesianVARs\")"},{"path":"https://luisgruber.github.io/bayesianVARs/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"MCMC Estimation of Bayesian Vectorautoregressions","text":"main workhorse conduct Bayesian inference vectorautoregression models package function bvar(). features: Prediction, plotting, extraction model parameters extraction fitted values usual generic functions predict(), plot(), coef(), vcov() fitted(). Configure prior distributions helper functions specify_prior_phi() specify_prior_sigma().","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/index.html","id":"demonstration","dir":"","previous_headings":"","what":"Demonstration","title":"MCMC Estimation of Bayesian Vectorautoregressions","text":"","code":"set.seed(537) # load package library(bayesianVARs)  # Load data train_data <-100 * usmacro_growth[1:237,c(\"GDPC1\", \"PCECC96\", \"GPDIC1\", \"AWHMAN\", \"GDPCTPI\", \"CES2000000008x\", \"FEDFUNDS\", \"GS10\", \"EXUSUKx\", \"S&P 500\")] test_data <-100 * usmacro_growth[238:241,c(\"GDPC1\", \"PCECC96\", \"GPDIC1\", \"AWHMAN\", \"GDPCTPI\", \"CES2000000008x\", \"FEDFUNDS\", \"GS10\", \"EXUSUKx\", \"S&P 500\")]                                     # Estimate model using default prior settings mod <- bvar(train_data, lags = 2L, draws = 2000, burnin = 1000, sv_keep = \"all\")  # Out of sample prediction and log-predictive-likelihood evaluation pred <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test_data)  # Visualize in-sample fit plus out-of-sample prediction intervals plot(mod, predictions = pred)"},{"path":"https://luisgruber.github.io/bayesianVARs/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"MCMC Estimation of Bayesian Vectorautoregressions","text":"bayesianVARs - Shrinkage Priors Bayesian Vectorautoregressions R","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"bvar simulates joint posterior distribution parameters latent variables returns posterior draws.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"","code":"bvar(   data,   lags = 1L,   draws = 1000L,   burnin = 1000L,   thin = 1L,   prior_intercept = 100,   prior_phi = specify_prior_phi(data = data, lags = lags, prior = \"HS\"),   prior_sigma = specify_prior_sigma(data = data, type = \"factor\", quiet = TRUE),   sv_keep = \"last\",   quiet = FALSE,   startvals = list(),   expert = list() )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"data Data matrix (can time series object). \\(M\\) columns assumed contain single time-series length \\(T\\). lags Integer indicating order VAR, .e. number lags dependent variables included predictors. draws single integer indicating number draws burnin burnin single integer indicating number draws discarded burnin thin single integer. Every \\(thin\\)th draw stored. Default thin=1L. prior_intercept Either prior_intercept=FALSE constant term (intercept) included. numeric vector length \\(M\\) indicating (fixed) prior variances constant term. single number recycled accordingly. Default prior_intercept=100. prior_phi bayesianVARs_prior_phi object specifying prior reduced form VAR coefficients. Best use constructor specify_prior_phi. prior_sigma bayesianVARs_prior_sigma object specifying prior variance-covariance matrix VAR. Best use constructor specify_prior_sigma. sv_keep String equal \"\" \"last\". case sv_keep = \"last\", default, draws last log-variance \\(h_T\\) stored. quiet logical value indicating whether information progress sampling displayed sampling (default TRUE). startvals optional list starting values. expert optional list expert settings.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"object type bayesianVARs_bvar, list containing following objects: PHI: bayesianVARs_coef object, array, containing posterior draws VAR coefficients (including intercept). U: bayesianVARs_draws object, matrix, containing posterior draws contemporaneous coefficients (cholesky decomposition sigma specified). logvar: bayesianVARs_draws object containing log-variance draws. sv_para: baysesianVARs_draws object containing posterior draws stochastic volatility related parameters. phi_hyperparameter: matrix containing posterior draws hyperparameters conditional normal prior VAR coefficients. u_hyperparameter: matrix containing posterior draws hyperparameters conditional normal prior U (cholesky decomposition sigma specified). bench: Numerical indicating average time took generate one single draw joint posterior distribution parameters. V_prior: array containing posterior draws variances conditional normal prior VAR coefficients. facload: bayesianVARs_draws object, array, containing draws posterior distribution factor loadings matrix (factor decomposition sigma specified). fac: bayesianVARs_draws object, array, containing factor draws posterior distribution (factor decomposition sigma specified). Y: Matrix containing dependent variables used estimation. X matrix containing lagged values dependent variables, .e. covariates. lags: Integer indicating lag order VAR. intercept: Logical indicating whether constant term included. heteroscedastic logical indicating whether heteroscedasticity assumed. Yraw: Matrix containing dependent variables, including initial 'lags' observations. Traw: Integer indicating total number observations. sigma_type: Character specifying decomposition variance-covariance matrix. datamat: Matrix containing 'Y' 'X'. config: List containing information configuration parameters.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"VAR(p) model following form: \\( \\boldsymbol{y}^\\prime_t = \\boldsymbol{\\iota}^\\prime + \\boldsymbol{x}^\\prime_t\\boldsymbol{\\Phi} + \\boldsymbol{\\epsilon}^\\prime_t\\), \\(\\boldsymbol{y}_t\\) \\(M\\)-dimensional vector dependent variables \\(\\boldsymbol{\\epsilon}_t\\) error term dimension. \\(\\boldsymbol{x}_t\\) \\(K=pM\\)-dimensional vector containing lagged/past values dependent variables \\(\\boldsymbol{y}_{t-l}\\) \\(l=1,\\dots,p\\) \\(\\boldsymbol{\\iota}\\) constant term (intercept) dimension \\(M\\times 1\\). reduced-form coefficient matrix \\(\\boldsymbol{\\Phi}\\) dimension \\(K \\times M\\). bvar offers two different specifications errors: user can choose factor stochastic volatility structure cholesky stochastic volatility structure. cases disturbances \\(\\boldsymbol{\\epsilon}_t\\) assumed follow \\(M\\)-dimensional multivariate normal distribution zero mean variance-covariance matrix \\(\\boldsymbol{\\Sigma}_t\\). case cholesky specification \\(\\boldsymbol{\\Sigma}_t = \\boldsymbol{U}^{\\prime -1} \\boldsymbol{D}_t \\boldsymbol{U}^{-1}\\), \\(\\boldsymbol{U}^{-1}\\) upper unitriangular (ones diagonal). diagonal matrix \\(\\boldsymbol{D}_t\\) depends upon latent log-variances, .e. \\(\\boldsymbol{D}_t=diag(exp(h_{1t}),\\dots, exp(h_{Mt})\\). log-variances follow priori independent autoregressive processes \\(h_{}\\sim N(\\mu_i + \\phi_i(h_{,t-1}-\\mu_i),\\sigma_i^2)\\) \\(=1,\\dots,M\\). case factor structure, \\(\\boldsymbol{\\Sigma}_t = \\boldsymbol{\\Lambda} \\boldsymbol{V}_t \\boldsymbol{\\Lambda}^\\prime + \\boldsymbol{G}_t\\). diagonal matrices \\(\\boldsymbol{V}_t\\) \\(\\boldsymbol{G}_t\\) depend upon latent log-variances, .e. \\(\\boldsymbol{G}_t=diag(exp(h_{1t}),\\dots, exp(h_{Mt})\\) \\(\\boldsymbol{V}_t=diag(exp(h_{M+1,t}),\\dots, exp(h_{M+r,t})\\). log-variances follow priori independent autoregressive processes \\(h_{}\\sim N(\\mu_i + \\phi_i(h_{,t-1}-\\mu_i),\\sigma_i^2)\\) \\(=1,\\dots,M\\) \\(h_{M+j,t}\\sim N(\\phi_ih_{M+j,t-1},\\sigma_{M+j}^2)\\) \\(j=1,\\dots,r\\).","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"mcmc-algorithm","dir":"Reference","previous_headings":"","what":"MCMC algorithm","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"sample efficiently reduced-form VAR coefficients assuming factor structure errors, equation per equation algorithm Kastner & Huber (2020) implemented. parameters latent variables associated factor-structure sampled using package factorstochvol-package's function update_fsv callable C-level . sample efficiently reduced-form VAR coefficients, assuming cholesky-structure errors, corrected triangular algorithm Carriero et al. (2021) implemented. SV parameters latent variables sampled using package stochvol's update_fast_sv function. precision parameters, .e. free -diagonal elements \\(\\boldsymbol{U}\\), sampled Cogley Sargent (2005).","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"Gruber, L. Kastner, G. (2023). Forecasting macroeconomic data Bayesian VARs: Sparse dense? depends! arXiv:2206.04902. Kastner, G. Huber, F. Sparse (2020). Bayesian vector autoregressions huge dimensions. Journal Forecasting. 39, 1142--1165, doi:10.1002/.2680 . Kastner, G. (2019). Sparse Bayesian Time-Varying Covariance Estimation Many Dimensions Journal Econometrics, 210(1), 98--115, doi:10.1016/j.jeconom.2018.11.007 . Carriero, . Chan, J.  Clark, T. E. Marcellino, M. (2021). Corrigendum “Large Bayesian vector autoregressions stochastic volatility non-conjugate priors” [J. Econometrics 212 (1) (2019) 137–154]. Journal Econometrics, doi:10.1016/j.jeconom.2021.11.010 . Cogley, S. Sargent, T. (2005). Drifts volatilities: monetary policies outcomes post WWII US. Review Economic Dynamics, 8, 262--302, doi:10.1016/j.red.2004.10.009 . Hosszejni, D. Kastner, G. (2021). Modeling Univariate Multivariate Stochastic Volatility R stochvol factorstochvol. Journal Statistical Software, 100, 1–-34. doi:10.18637/jss.v100.i12 .","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Plot plot(mod)   # Summary summary(mod) #>  #> Posterior median of reduced-form coefficients: #>                 GDPC1 CPIAUCSL   FEDFUNDS #> GDPC1.l1     0.236588 0.006926  2.109e-02 #> CPIAUCSL.l1 -0.053390 0.616162 -3.646e-03 #> FEDFUNDS.l1  0.007874 0.037088  1.001e+00 #> intercept    0.005874 0.001291 -8.055e-05 #>  #> Posterior interquartile range of of reduced-form coefficients: #>                GDPC1  CPIAUCSL  FEDFUNDS #> GDPC1.l1    0.089598 0.0290897 0.0252782 #> CPIAUCSL.l1 0.111515 0.0904426 0.0150976 #> FEDFUNDS.l1 0.018416 0.0141909 0.0076726 #> intercept   0.001135 0.0007528 0.0001651"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract VAR coefficients — coef","title":"Extract VAR coefficients — coef","text":"Extracts posterior draws VAR coefficients VAR model estimated bvar().","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract VAR coefficients — coef","text":"","code":"# S3 method for bayesianVARs_bvar coef(object, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract VAR coefficients — coef","text":"object bayesianVARs_bvar object obtained bvar(). ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract VAR coefficients — coef","text":"Returns numeric array dimension \\(M \\times K \\times draws\\), M number time-series, K number covariates per equation (including intercept) draws number stored posterior draws.","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract VAR coefficients — coef","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Extract posterior draws of VAR coefficients bvar_coefs <- coef(mod)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/fitted.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","title":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","text":"Simulates fitted/predicted (-sample) values estimated VAR model.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/fitted.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","text":"","code":"# S3 method for bayesianVARs_bvar fitted(object, error_term = TRUE, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/fitted.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","text":"object bayesianVARs_bvar object estimated via bvar(). error_term logical indicating whether include error term . ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/fitted.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","text":"object class bayesianVARs_fitted.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/fitted.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Simulate predicted historical values including the error term. pred <- fitted(mod, error_term = TRUE)  # Simulate fitted historical values not including the error term. fit <- fitted(mod, error_term = FALSE)  # Visualize plot(pred)  plot(fit)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/my_gig.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw from generalized inverse Gaussian — my_gig","title":"Draw from generalized inverse Gaussian — my_gig","text":"Vectorized version rgig","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/my_gig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw from generalized inverse Gaussian — my_gig","text":"","code":"my_gig(n, lambda, chi, psi)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/my_gig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw from generalized inverse Gaussian — my_gig","text":"n single integer indicating number draws generate. lambda vector shape parameters. chi vector shape/scale parameters. Must nonnegative positive lambdas positive else. psi vector shape/scale parameters. Must nonnegative negative lambdas positive else.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/my_gig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw from generalized inverse Gaussian — my_gig","text":"Matrix dimension c(n,m), m maximum length lambda, psi chi.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/my_gig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw from generalized inverse Gaussian — my_gig","text":"","code":"gigsamples <- my_gig(2, c(1,1), c(1,1), c(1,1))"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"Pairwise visualization --sample posterior predictive densities.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"","code":"# S3 method for bayesianVARs_predict pairs(x, vars, ahead, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"x object class bayesianVARs_predict obtained via predict.bayesianVARs_bvar(). vars Integer vector (coercible ) indicating variables plot. ahead Integer vector (coercible ) indicating step ahead plot. max(ahead) must smaller equal dim(x$predictions)[1]. ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"Returns x invisibly.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"Note bayesianVARs_predict can also used withing plot.bayesianVARs_bvar().","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Simulate from posterior predictive predictions <- predict(mod, ahead = 1:3) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  485 stable posterior draws remaining for prediction!  # Visualize pairs(predictions, vars = 1:3, ahead = 1:3)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","title":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","text":"Visualization -sample fit. Can also used display prediction intervals future values.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","text":"","code":"# S3 method for bayesianVARs_bvar plot(   x,   predictions = NULL,   quantiles = c(0.05, 0.5, 0.95),   dates = NULL,   n_col = 1,   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","text":"x object class bayesianVARs_bvar obtained via bvar(). predictions Optional array sample predictions, e.g. obtained via predict.bayesianVARs_bvar(). quantiles numeric vector indicating quantiles plot. dates optional vector dates labelling x-axis. default values NULL; case, axis labeled numbers. n_col integer indicating number columns use plotting. ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","text":"Returns x invisibly.","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Simulate from posterior predictive predictions <- predict(mod, ahead = 1:3) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  626 stable posterior draws remaining for prediction!  # Visualize plot(mod, predictions = predictions)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_fitted.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","title":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","text":"Visualization -sample fit estimated VAR.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_fitted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","text":"","code":"# S3 method for bayesianVARs_fitted plot(   x,   dates = NULL,   vars = \"all\",   quantiles = c(0.05, 0.5, 0.95),   n_col = 1L,   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_fitted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","text":"x bayesianVARs_fitted object. dates optional vector dates labelling x-axis. default values NULL; case, axis labeled numbers. vars character vector containing names variables visualized. default \"\" indicating fit variables visualized. quantiles numeric vector indicating quantiles plot. n_col integer indicating number columns use plotting. ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_fitted.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","text":"returns x invisibly","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_fitted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Simulate predicted historical values including the error term. pred <- fitted(mod, error_term = TRUE)  # Visualize plot(pred)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Fan chart — plot.bayesianVARs_predict","title":"Fan chart — plot.bayesianVARs_predict","text":"Visualization (--sample) predictive distribution.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fan chart — plot.bayesianVARs_predict","text":"","code":"# S3 method for bayesianVARs_predict plot(   x,   dates = NULL,   vars = \"all\",   ahead = NULL,   quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),   n_col = 1L,   first_obs = 1L,   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fan chart — plot.bayesianVARs_predict","text":"x object type bayesianVARs_predict obtained via predict.bayesianVARs_bvar(). dates optional vector dates labeling x-axis. default values NULL; case, axis labeled numbers. vars character vector containing names variables visualized. default \"\" indicating variables visualized. ahead Integer vector (coercible ) indicating step ahead plot. max(ahead) must smaller equal dim(x$predictions)[1]. quantiles numeric vector indicating quantiles plot. n_col integer indicating number columns use plotting. first_obs integer indicating first observation used plotting. ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fan chart — plot.bayesianVARs_predict","text":"Returns x invisibly!","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fan chart — plot.bayesianVARs_predict","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Simulate from posterior predictive predictions <- predict(mod, ahead = 1:3) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  385 stable posterior draws remaining for prediction!  # Visualize plot(predictions, vars = 1:3, ahead = 1:3)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/posterior_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","title":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","text":"Posterior heatmaps VAR coefficients variance-covariance matrices","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/posterior_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","text":"","code":"posterior_heatmap(   x,   FUN,   ...,   colorbar = TRUE,   xlabels = NULL,   ylabels = NULL,   add_numbers = FALSE,   zlim = NULL,   colspace = NULL,   main = \"\",   cex.axis = 0.75,   cex.colbar = 1,   cex.numbers = 1,   asp = NULL )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/posterior_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","text":"x array dimension \\(\\times b \\times draws\\), \\(  \\times b\\) dimension parameter visualize draws number posterior draws. FUN summary function applied margins c(1,2) x. E.g. \"median\", \"mean\", \"IQR\", \"sd\" \"var\". apply(x, 1:2, FUN, ...) must return matrix! ... optional arguments FUN. colorbar logical indicating whether display colorbar . Default TRUE. xlabels ylabels=NULL, default, indicates names dependent variables displayed. ylabels=\"\" indicates ylabels displayed. ylabels xlabels=NULL, default, indicates labels covariables (lagged values dependent variables) displayed. xlabels=\"lags\" indicates lags marked. xlabels=\"\" indicates ylabels displayed. add_numbers logical. add_numbers=TRUE, default indicates actual values summary displayed. zlim numeric vector length two indicating minimum maximum values colors plotted. default range determined maximum absolute values selected summary. colspace Optional argument. main main title plot. cex.axis magnification used y-axis annotation relative current setting cex. cex.colbar magnification used colorbar annotation relative current setting cex. cex.numbers magnification used actual values (add_numbers=TRUE) relative current setting cex. asp aspect ratio.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/posterior_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","text":"Returns x invisibly.","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/posterior_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(100*data, sv_keep = \"all\", quiet = TRUE)  # Extract posterior draws of VAR coefficients phi_post <- coef(mod)  # Visualize posterior median of VAR coefficients posterior_heatmap(phi_post, median)   # Extract posterior draws of variance-covariance matrices (for each point in time) sigma_post <- vcov(mod) # Visualize posterior interquartile-range of variance-covariance matrix of the first observation posterior_heatmap(sigma_post[1,,,], IQR)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/predict.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","title":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","text":"Simulates (--sample) predictive density Bayesian VARs estimated via bvar() computes log predictive likelhoods ex-post observed data supplied.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/predict.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","text":"","code":"# S3 method for bayesianVARs_bvar predict(   object,   ahead = 1L,   each = 1L,   stable = TRUE,   simulate_predictive = TRUE,   LPL = FALSE,   Y_obs = NA,   LPL_VoI = NA,   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/predict.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","text":"object bayesianVARs_bvar object, obtained bvar(). ahead Integer vector (coercible ), indicating number steps ahead predict. Single integer (coercible ) indicating often drawn posterior predictive distribution draw stored MCMC sampling. stable logical indicating whether consider draws posterior fulfill 'stable' criterion. Default TRUE. simulate_predictive logical, indicating whether posterior predictive distribution simulated. LPL logical indicating whether ahead-step-ahead log predictive likelihoods computed. LPL=TRUE, Y_obs specified. Y_obs Data matrix observed values computation log predictive likelihood. ncol(object$Yraw) columns assumed contain single time-series length length(ahead). LPL_VoI either integer vector character vector column-names indicating subgroup time-series object$Yraw joint log predictive likelihood shall computed. ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/predict.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","text":"Object class bayesianVARs_predict, list may contain following elements: predictions array dimensions c(length(ahead), ncol(object$Yraw), * dim(object$PHI)[3]) containing simulations predictive density (simulate_predictive=TRUE). LPL vector length length(ahead) containing log-predictive-likelihoods (taking account joint distribution variables) (LPL=TRUE). LPL_univariate matrix dimension c(length(ahead), ncol(object$Yraw) containing marginalized univariate log-predictive-likelihoods series (LPL=TRUE). LPL_VoI vector length length(ahead) containing log-predictive-likelihoods subset variables (LPL=TRUE LPL_VoI != NA). Yraw matrix containing data used estimation VAR. LPL_draws matrix containing simulations log-predictive-likelihood (LPL=TRUE). PL_univariate_draws array containing simulations univariate predictive-likelihoods (LPL=TRUE). LPL_sub_draws matrix containing simulations log-predictive-likelihood subset variables (LPL=TRUE LPL_VoI != NA).","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/predict.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Split data in train and test train <- data[1:(nrow(data)-4),] test <- data[-c(1:(nrow(data)-4)),]  # Estimate model using train data only mod <- bvar(train, quiet = TRUE)  # Simulate from 1-step to 4-steps ahead posterior predictive and compute # log-predictive-likelihoods predictions <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  617 stable posterior draws remaining for prediction!  # Summary summary(predictions) #>  #> LPL: #>   t+1   t+2   t+3   t+4  #> 5.114 9.665 9.092 6.526  #>  #> Marginal univariate LPLs: #>       GDPC1 CPIAUCSL FEDFUNDS #> t+1 -0.8729   0.4109    3.541 #> t+2  2.8555   2.6739    3.391 #> t+3  2.8363   2.4370    3.349 #> t+4  2.8931   0.2959    3.326 #>  #> Prediction quantiles: #> , , GDPC1 #>  #>          t+1        t+2       t+3       t+4 #> 5%  -0.07875 -0.0329135 -0.021393 -0.011870 #> 50% -0.01851  0.0009099  0.005688  0.006618 #> 95%  0.03798  0.0429166  0.034118  0.024602 #>  #> , , CPIAUCSL #>  #>           t+1       t+2       t+3        t+4 #> 5%  -0.018515 -0.017824 -0.015592 -0.0140807 #> 50% -0.007656 -0.005287 -0.002413 -0.0005886 #> 95%  0.002679  0.006747  0.009743  0.0106464 #>  #> , , FEDFUNDS #>  #>           t+1       t+2       t+3       t+4 #> 5%  -0.016214 -0.022544 -0.026463 -0.030055 #> 50% -0.004527 -0.005249 -0.006258 -0.006404 #> 95%  0.005912  0.011279  0.016238  0.018100 #>   # Visualize via fan-charts plot(predictions)   # \\donttest{ # In order to evaluate the joint predictive density of a subset of the # variables (variables of interest), consider specifying 'LPL_VoI': predictions <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test, LPL_VoI = c(\"GDPC1\",\"FEDFUNDS\")) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  617 stable posterior draws remaining for prediction! predictions$LPL_VoI #>      t+1      t+2      t+3      t+4  #> 2.542386 6.712653 6.548112 6.391536  # }"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Pretty printing of a bvar object — print.bayesianVARs_bvar","title":"Pretty printing of a bvar object — print.bayesianVARs_bvar","text":"Pretty printing bvar object","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pretty printing of a bvar object — print.bayesianVARs_bvar","text":"","code":"# S3 method for bayesianVARs_bvar print(x, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pretty printing of a bvar object — print.bayesianVARs_bvar","text":"x Object class bayesianVARs_bvar, usually resulting call bvar(). ... Ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pretty printing of a bvar object — print.bayesianVARs_bvar","text":"Returns x invisibly.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pretty printing of a bvar object — print.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Print model mod #>  #> Fitted bayesianVARs_bvar object with #>   -       3 series #>   -       1 lag(s) #>   -     246 used observations #>   -     247 total observations #>   -    1000 MCMC draws #>   -       1 thinning #>   -    1000 burn-in #>"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","title":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","text":"Print method bayesianVARs_predict objects.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","text":"","code":"# S3 method for bayesianVARs_predict print(x, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","text":"x bayesianVARs_predict object obtained via predict.bayesianVARs_bvar(). ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","text":"Returns x invisibly.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Split data in train and test train <- data[1:(nrow(data)-4),] test <- data[-c(1:(nrow(data)-4)),]  # Estimate model using train data only mod <- bvar(train, quiet = TRUE)  # Simulate from 1-step ahead posterior predictive predictions <- predict(mod, ahead = 1L) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  595 stable posterior draws remaining for prediction! print(predictions) #>  #> Generic functions for bayesianVARs_predict objects: #>   - summary.bayesianVARs_predict(), #>   - pairs.bayesianVARs_predict(), #>   - plot.bayesianVARs_predict() (alias for pairs.bayesianVARs_predict())."},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","title":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","text":"Print method summary.bayesianVARs_bvar objects.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","text":"","code":"# S3 method for summary.bayesianVARs_bvar print(x, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","text":"x summary.bayesianVARs_bvar object obtained via summary.bayesianVARs_bvar(). ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","text":"Returns x invisibly!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate model mod <- bvar(data, quiet = TRUE)  # Print summary summary(mod) #>  #> Posterior median of reduced-form coefficients: #>                 GDPC1 CPIAUCSL   FEDFUNDS #> GDPC1.l1     0.226152  0.00604  1.362e-02 #> CPIAUCSL.l1 -0.063468  0.62045 -4.773e-03 #> FEDFUNDS.l1  0.009492  0.03717  1.001e+00 #> intercept    0.005959  0.00120 -4.555e-05 #>  #> Posterior interquartile range of of reduced-form coefficients: #>                GDPC1  CPIAUCSL  FEDFUNDS #> GDPC1.l1    0.105445 0.0271618 0.0208810 #> CPIAUCSL.l1 0.118059 0.0930382 0.0131568 #> FEDFUNDS.l1 0.019176 0.0145773 0.0065391 #> intercept   0.001173 0.0007342 0.0001668"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","title":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","text":"Print method summary.bayesianVARs_predict objects.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","text":"","code":"# S3 method for summary.bayesianVARs_predict print(x, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","text":"x summary.bayesianVARs_predict object obtained via summary.bayesianVARs_predict(). ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","text":"Returns x invisibly.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Split data in train and test train <- data[1:(nrow(data)-4),] test <- data[-c(1:(nrow(data)-4)),]  # Estimate model using train data only mod <- bvar(train, quiet = TRUE)  # Simulate from 1-step ahead posterior predictive predictions <- predict(mod, ahead = 1L) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  586 stable posterior draws remaining for prediction! sum <- summary(predictions) print(sum) #>  #> Prediction quantiles: #> , , GDPC1 #>  #>          t+1 #> 5%  -0.06812 #> 50% -0.01798 #> 95%  0.02909 #>  #> , , CPIAUCSL #>  #>           t+1 #> 5%  -0.018438 #> 50% -0.008339 #> 95%  0.003312 #>  #> , , FEDFUNDS #>  #>           t+1 #> 5%  -0.021399 #> 50% -0.004006 #> 95%  0.009963 #>"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify prior on PHI — specify_prior_phi","title":"Specify prior on PHI — specify_prior_phi","text":"Configures prior PHI, matrix reduced-form VAR coefficients.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify prior on PHI — specify_prior_phi","text":"","code":"specify_prior_phi(   data = NULL,   M = ncol(data),   lags = 1L,   prior = \"HS\",   priormean = 0,   PHI_tol = 1e-18,   DL_a = \"1/K\",   DL_tol = 0,   R2D2_a = 0.1,   R2D2_b = 0.5,   R2D2_tol = 0,   NG_a = 0.1,   NG_b = 1,   NG_c = 1,   NG_tol = 0,   SSVS_c0 = 0.01,   SSVS_c1 = 100,   SSVS_semiautomatic = TRUE,   SSVS_p = 0.5,   HMP_lambda1 = c(0.01, 0.01),   HMP_lambda2 = c(0.01, 0.01),   normal_sds = 10,   global_grouping = \"global\",   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify prior on PHI — specify_prior_phi","text":"data Optional. Data matrix (can time series object). \\(M\\) columns assumed contain single time-series length \\(T\\). M positive integer indicating number time-series VAR. lags positive integer indicating order VAR, .e. number lags dependent variables included predictors. prior character, one \"HS\", \"R2D2\", \"NG\", \"DL\", \"SSVS\", \"HMP\" \"normal\". priormean real numbers indicating prior means VAR coefficients. One single number means prior mean -lag coefficients w.r.t. first lag equals priormean 0 else. vector length M means prior mean -lag coefficients w.r.t. first lag equals priormean 0 else. priormean matrix dimension c(lags*M,M), \\(M\\) columns assumed contain lags*M prior means VAR coefficients respective VAR equations. PHI_tol Minimum number absolute value VAR coefficient draw can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. DL_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. argument global_grouping specifies e.g. k groups, DL_a can numeric vector length k elements indicate shrinkage group.  matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. DL_a specified prior=\"DL\". DL_tol Minimum number parameter draw one shrinking parameters Dirichlet Laplace prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. DL_tol specified prior=\"DL\". R2D2_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. argument global_grouping specifies e.g. k groups, R2D2_a can numeric vector length k elements indicate shrinkage group.  matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. R2D2_a specified prior=\"R2D2\". R2D2_b (Single) positive real number. value indicates shape parameter inverse gamma prior (semi-)global scales. argument global_grouping specifies e.g. k groups, NG_b can numeric vector length k elements determine shape parameter group. R2D2_b specified prior=\"R2D2\". R2D2_tol Minimum number parameter draw one shrinking parameters R2D2 prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. R2D2_tol specified prior=\"R2D2\". NG_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. argument global_grouping specifies e.g. k groups, NG_a can numeric vector length k elements indicate shrinkage group.  matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. NG_a specified prior=\"NG\". NG_b (Single) positive real number. value indicates shape parameter inverse gamma prior (semi-)global scales. argument global_grouping specifies e.g. k groups, NG_b can numeric vector length k elements determine shape parameter group. NG_b specified prior=\"NG\". NG_c (Single) positive real number. value indicates scale parameter inverse gamma prior (semi-)global scales. argument global_grouping specifies e.g. k groups, NG_c can numeric vector length k elements determine scale parameter group. Expert option set scale parameter proportional NG_a. E.g. case discrete hyperprior NG_a chosen, desired proportion say 0.2 achieved setting NG_c=\"0.2a\" (character input!). NG_c specified prior=\"NG\". NG_tol Minimum number parameter draw one shrinking parameters normal-gamma prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. NG_tol specified prior=\"NG\". SSVS_c0 single positive number indicating (unscaled) standard deviation spike component. SSVS_c0 specified prior=\"SSVS\". \\(SSVS_{c0} \\ll SSVS_{c1}\\)! SSVS_c0 specified prior=\"SSVS\". SSVS_c1 single positive number indicating (unscaled) standard deviation slab component. SSVS_c0 specified prior=\"SSVS\". \\(SSVS_{c0} \\ll SSVS_{c1}\\)! SSVS_semiautomatic logical. SSVS_semiautomatic=TRUE SSVS_c0 SSVS_c1 scaled variances posterior PHI FLAT conjugate (dependent Normal-Wishart prior). SSVS_semiautomatic specified prior=\"SSVS\". SSVS_p Either single positive number range (0,1) indicating (fixed) prior inclusion probability coefficient. numeric vector length 2 positive entries indicating shape parameters Beta distribution. case Beta hyperprior placed prior inclusion probability. SSVS_p specified prior=\"SSVS\". HMP_lambda1 numeric vector length 2. entries must positive. first indicates shape second rate Gamma hyperprior -lag coefficients. HMP_lambda1 specified prior=\"HMP\". HMP_lambda2 numeric vector length 2. entries must positive. first indicates shape second rate Gamma hyperprior cross-lag coefficients. HMP_lambda2 specified prior=\"HMP\". normal_sds numeric vector length \\(n\\), \\(n = lags M^2\\) number VAR coefficients (excluding intercept), indicating prior variances. single number recycled accordingly! Must positive. normal_sds specified prior=\"normal\". global_grouping One \"global\", \"equation-wise\", \"covariate-wise\", \"olcl-lagwise\" \"fol\" indicating sub-groups semi-global(-local) modifications HS, R2D2, NG, DL SSVS prior. Works also user-specified indicator matrix dimension c(lags*M,M).  relevant prior=\"HS\", prior=\"DL\", prior=\"R2D2\", prior=\"NG\" prior=\"SSVS\". ... use!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify prior on PHI — specify_prior_phi","text":"baysianVARs_prior_phi-object.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify prior on PHI — specify_prior_phi","text":"details concerning prior-elicitation VARs please see Gruber & Kastner (2023). Currently one can choose six hierarchical shrinkage priors normal prior: prior=\"HS\" stands Horseshoe-prior, prior=\"R2D2 R\\(^2\\)-induced-Dirichlet-decompostion-prior, prior=\"NG\" normal-gamma-prior, prior=\"DL\" Dirichlet-Laplace-prior, prior=\"SSVS\" stochastic-search-variable-selection-prior, prior=\"HMP\" semi-hierarchical Minnesota prior prior=normal normal-prior. Semi-global shrinkage, .e. group-specific shrinkage pre-specified subgroups coefficients, can achieved argument global_grouping.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Specify prior on PHI — specify_prior_phi","text":"Gruber, L. Kastner, G. (2023). Forecasting macroeconomic data Bayesian VARs: Sparse dense? depends! arXiv:2206.04902.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify prior on PHI — specify_prior_phi","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Horseshoe prior for a VAR(2) phi_hs <- specify_prior_phi(data = data, lags = 2L ,prior = \"HS\")  # Semi-global-local Horseshoe prior for a VAR(2) with semi-global shrinkage parameters for # cross-lag and own-lag coefficients in each lag phi_hs_sg <- specify_prior_phi(data = data, lags = 2L, prior = \"HS\", global_grouping = \"olcl-lagwise\")  # Semi-global-local Horseshoe prior for a VAR(2) with equation-wise shrinkage # construct indicator matrix for equation-wise shrinkage semi_global_mat <- matrix(1:ncol(data), 2*ncol(data), ncol(data), byrow = TRUE) phi_hs_ew <- specify_prior_phi(data = data, lags = 2L, prior = \"HS\", global_grouping = semi_global_mat) # (for equation-wise shrinkage one can also use 'global_grouping = \"equation-wise\"')  # \\donttest{ # Estimate model with your prior configuration of choice mod <- bvar(data, lags = 2L, prior_phi = phi_hs_sg, quiet = TRUE) # }"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify prior on Sigma — specify_prior_sigma","title":"Specify prior on Sigma — specify_prior_sigma","text":"Configures prior variance-covariance VAR.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify prior on Sigma — specify_prior_sigma","text":"","code":"specify_prior_sigma(   data = NULL,   M = ncol(data),   type = c(\"factor\", \"cholesky\"),   factor_factors = 1L,   factor_restrict = c(\"none\", \"upper\"),   factor_priorfacloadtype = c(\"rowwiseng\", \"colwiseng\", \"normal\"),   factor_priorfacload = 0.1,   factor_facloadtol = 1e-18,   factor_priorng = c(1, 1),   factor_priormu = c(0, 10),   factor_priorphiidi = c(10, 3),   factor_priorphifac = c(10, 3),   factor_priorsigmaidi = 1,   factor_priorsigmafac = 1,   factor_priorh0idi = \"stationary\",   factor_priorh0fac = \"stationary\",   factor_heteroskedastic = TRUE,   factor_priorhomoskedastic = NA,   factor_interweaving = 4,   cholesky_U_prior = c(\"HS\", \"DL\", \"R2D2\", \"NG\", \"SSVS\", \"normal\", \"HMP\"),   cholesky_U_tol = 1e-18,   cholesky_heteroscedastic = TRUE,   cholesky_priormu = c(0, 100),   cholesky_priorphi = c(20, 1.5),   cholesky_priorsigma2 = c(0.5, 0.5),   cholesky_priorh0 = \"stationary\",   cholesky_priorhomoscedastic = as.numeric(NA),   cholesky_DL_a = \"1/n\",   cholesky_DL_tol = 0,   cholesky_R2D2_a = 0.4,   cholesky_R2D2_b = 0.5,   cholesky_R2D2_tol = 0,   cholesky_NG_a = 0.5,   cholesky_NG_b = 0.5,   cholesky_NG_c = 0.5,   cholesky_NG_tol = 0,   cholesky_SSVS_c0 = 0.001,   cholesky_SSVS_c1 = 1,   cholesky_SSVS_p = 0.5,   cholesky_HMP_lambda3 = c(0.01, 0.01),   cholesky_normal_sds = 10,   expert_sv_offset = 0,   quiet = FALSE,   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify prior on Sigma — specify_prior_sigma","text":"data Optional. Data matrix (can time series object). \\(M\\) columns assumed contain single time-series length \\(T\\). M positive integer indicating number time-series VAR. type character, one \"factor\" (default) \"cholesky\", indicating decomposition applied covariance-matrix. factor_factors Number latent factors estimated. required type=\"factor\". factor_restrict Either \"upper\" \"none\", indicating whether factor loadings matrix restricted zeros diagonal (\"upper\") whether elements estimated data (\"none\"). Setting restrict \"upper\" often stabilizes MCMC estimation can important identifying factor loadings matrix, however, generally strong prior assumption. Setting restrict \"none\" usually preferred option identification factor loadings matrix less concern covariance estimation prediction goal. required type=\"factor\". factor_priorfacloadtype Can \"normal\", \"rowwiseng\", \"colwiseng\". required type=\"factor\". \"normal\": Normal prior. value priorfacload interpreted standard deviations Gaussian prior distributions factor loadings. \"rowwiseng\": Row-wise Normal-Gamma prior. value priorfacload interpreted shrinkage parameter . \"colwiseng\": Column-wise Normal-Gamma prior. value priorfacload interpreted shrinkage parameter . details please see Kastner (2019). factor_priorfacload Either matrix dimensions M times factor_factors positive elements single number (recycled accordingly). required type=\"factor\". meaning factor_priorfacload depends setting factor_priorfacloadtype explained . factor_facloadtol Minimum number absolute value factor loadings draw can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. required type=\"factor\". factor_priorng Two-element vector positive entries indicating Normal-Gamma prior's hyperhyperparameters c d (cf. Kastner (2019)). required type=\"factor\". factor_priormu Vector length 2 denoting prior mean standard deviation unconditional levels idiosyncratic log variance processes. required type=\"factor\". factor_priorphiidi Vector length 2, indicating shape parameters Beta prior distributions transformed parameters (phi+1)/2, phi denotes persistence idiosyncratic log variances. required type=\"factor\". factor_priorphifac Vector length 2, indicating shape parameters Beta prior distributions transformed parameters (phi+1)/2, phi denotes persistence factor log variances. required type=\"factor\". factor_priorsigmaidi Vector length M containing prior volatilities log variances. factor_priorsigmaidi exactly one element, recycled idiosyncratic log variances. required type=\"factor\". factor_priorsigmafac Vector length factor_factors containing prior volatilities log variances. factor_priorsigmafac exactly one element, recycled factor log variances. required type=\"factor\". factor_priorh0idi Vector length 1 M, containing information Gaussian prior initial idiosyncratic log variances. required type=\"factor\". element factor_priorh0idi nonnegative number, conditional prior corresponding initial log variance h0 assumed Gaussian mean 0 standard deviation factor_priorh0idi times \\(sigma\\). element factor_priorh0idi string 'stationary', prior corresponding initial log volatility taken stationary distribution, .e. h0 assumed Gaussian mean 0 variance \\(sigma^2/(1-phi^2)\\). factor_priorh0fac Vector length 1 factor_factors, containing information Gaussian prior initial factor log variances. required type=\"factor\". element factor_priorh0fac nonnegative number, conditional prior corresponding initial log variance h0 assumed Gaussian mean 0 standard deviation factor_priorh0fac times \\(sigma\\). element factor_priorh0fac string 'stationary', prior corresponding initial log volatility taken stationary distribution, .e. h0 assumed Gaussian mean 0 variance \\(sigma^2/(1-phi^2)\\). factor_heteroskedastic Vector length 1, 2, M +  factor_factors, containing logical values indicating whether time-varying (factor_heteroskedastic = TRUE) constant (factor_heteroskedastic =   FALSE) variance estimated. factor_heteroskedastic length 2 recycled accordingly, whereby first element used idiosyncratic variances second element used factor variances. required type=\"factor\". factor_priorhomoskedastic used least one element factor_heteroskedastic set FALSE. case, factor_priorhomoskedastic must matrix positive entries dimension c(M, 2). Values column 1 interpreted shape values column 2 interpreted rate parameter corresponding inverse gamma prior distribution idiosyncratic variances. required type=\"factor\". factor_interweaving following values interweaving factor loadings accepted (required type=\"factor\"): 0: interweaving. 1: Shallow interweaving diagonal entries. 2: Deep interweaving diagonal entries. 3: Shallow interweaving largest absolute entries column. 4: Deep interweaving largest absolute entries column. details please see Kastner et al. (2017). value 4 highly recommended default. cholesky_U_prior character, one \"HS\", \"R2D2\", \"NG\", \"DL\", \"SSVS\", \"HMP\" \"normal\". required type=\"cholesky\". cholesky_U_tol Minimum number absolute value free -diagonal element \\(U\\)-draw can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. required type=\"cholesky\". cholesky_heteroscedastic single logical indicating whether time-varying (cholesky_heteroscedastic = TRUE) constant (cholesky_heteroscedastic = FALSE) variance estimated. required type=\"cholesky\". cholesky_priormu Vector length 2 denoting prior mean standard deviation unconditional levels log variance processes. required type=\"cholesky\". cholesky_priorphi Vector length 2, indicating shape parameters Beta prior distributions transformed parameters (phi+1)/2, phi denotes persistence log variances. required type=\"cholesky\". cholesky_priorsigma2 Vector length 2, indicating shape rate Gamma prior distributions variance log variance processes. (Currently one global setting \\(M\\) processes supported). required type=\"cholesky\". cholesky_priorh0 Vector length 1 M, containing information Gaussian prior initial idiosyncratic log variances. required type=\"cholesky\". element cholesky_priorh0 nonnegative number, conditional prior corresponding initial log variance h0 assumed Gaussian mean 0 standard deviation cholesky_priorh0 times \\(sigma\\). element cholesky_priorh0 string 'stationary', prior corresponding initial log volatility taken stationary distribution, .e. h0 assumed Gaussian mean 0 variance \\(sigma^2/(1-phi^2)\\). cholesky_priorhomoscedastic used cholesky_heteroscedastic=FALSE. case, cholesky_priorhomoscedastic must matrix positive entries dimension c(M, 2). Values column 1 interpreted shape values column 2 interpreted scale parameter corresponding inverse gamma prior distribution variances. required type=\"cholesky\". cholesky_DL_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. cholesky_DL_a specified cholesky_U_prior=\"DL\". cholesky_DL_tol Minimum number parameter draw one shrinking parameters Dirichlet Laplace prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. DL_tol specified cholesky_U_prior=\"DL\". cholesky_R2D2_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. cholesky_R2D2_a specified cholesky_U_prior=\"R2D2\". cholesky_R2D2_b single positive number, greater values indicate heavier regularization. cholesky_R2D2_b specified cholesky_U_prior=\"R2D2\". cholesky_R2D2_tol Minimum number parameter draw one shrinking parameters R2D2 prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. cholesky_R2D2_tol specified cholesky_U_prior=\"R2D2\". cholesky_NG_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. cholesky_NG_a specified cholesky_U_prior=\"NG\". cholesky_NG_b (Single) positive real number. value indicates shape parameter inverse gamma prior global scales. cholesky_NG_b specified cholesky_U_prior=\"NG\". cholesky_NG_c (Single) positive real number. value indicates scale parameter inverse gamma prior global scales. Expert option set scale parameter proportional NG_a. E.g. case discrete hyperprior NG_a chosen, desired proportion say 0.2 achieved setting NG_c=\"0.2a\" (character input!). cholesky_NG_c specified cholesky_U_prior=\"NG\". cholesky_NG_tol Minimum number parameter draw one shrinking parameters normal-gamma prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. cholesky_NG_tol specified cholesky_U_prior=\"NG\". cholesky_SSVS_c0 single positive number indicating (unscaled) standard deviation spike component. cholesky_SSVS_c0 specified choleksy_U_prior=\"SSVS\". \\(SSVS_{c0}   \\ll SSVS_{c1}\\)! cholesky_SSVS_c1 single positive number indicating (unscaled) standard deviation slab component. cholesky_SSVS_c1 specified choleksy_U_prior=\"SSVS\". \\(SSVS_{c0} \\ll SSVS_{c1}\\)! cholesky_SSVS_p Either single positive number range (0,1) indicating (fixed) prior inclusion probability coefficient. numeric vector length 2 positive entries indicating shape parameters Beta distribution. case Beta hyperprior placed prior inclusion probability. cholesky_SSVS_p specified choleksy_U_prior=\"SSVS\". cholesky_HMP_lambda3 numeric vector length 2. entries must positive. first indicates shape second rate Gamma hyperprior contemporaneous coefficients. cholesky_HMP_lambda3 specified choleksy_U_prior=\"HMP\". cholesky_normal_sds numeric vector length \\(\\frac{M^2-M}{2}\\), indicating prior variances free -diagonal elements \\(U\\). single number recycled accordingly! Must positive. cholesky_normal_sds specified choleksy_U_prior=\"normal\". expert_sv_offset ... use! quiet logical indicating whether informative output omitted. ... use!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify prior on Sigma — specify_prior_sigma","text":"Object class bayesianVARs_prior_sigma.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify prior on Sigma — specify_prior_sigma","text":"bvar offers two different specifications errors: user can choose factor stochastic volatility structure cholesky stochastic volatility structure. cases disturbances \\(\\boldsymbol{\\epsilon}_t\\) assumed follow \\(M\\)-dimensional multivariate normal distribution zero mean variance-covariance matrix \\(\\boldsymbol{\\Sigma}_t\\). case cholesky specification \\(\\boldsymbol{\\Sigma}_t = \\boldsymbol{U}^{\\prime -1} \\boldsymbol{D}_t \\boldsymbol{U}^{-1}\\), \\(\\boldsymbol{U}^{-1}\\) upper unitriangular (ones diagonal). diagonal matrix \\(\\boldsymbol{D}_t\\) depends upon latent log-variances, .e. \\(\\boldsymbol{D}_t=diag(exp(h_{1t}),\\dots, exp(h_{Mt})\\). log-variances follow priori independent autoregressive processes \\(h_{}\\sim N(\\mu_i + \\phi_i(h_{,t-1}-\\mu_i),\\sigma_i^2)\\) \\(=1,\\dots,M\\). case factor structure, \\(\\boldsymbol{\\Sigma}_t = \\boldsymbol{\\Lambda} \\boldsymbol{V}_t \\boldsymbol{\\Lambda}^\\prime + \\boldsymbol{G}_t\\). diagonal matrices \\(\\boldsymbol{V}_t\\) \\(\\boldsymbol{G}_t\\) depend upon latent log-variances, .e. \\(\\boldsymbol{G}_t=diag(exp(h_{1t}),\\dots, exp(h_{Mt})\\) \\(\\boldsymbol{V}_t=diag(exp(h_{M+1,t}),\\dots, exp(h_{M+r,t})\\). log-variances follow priori independent autoregressive processes \\(h_{}\\sim N(\\mu_i + \\phi_i(h_{,t-1}-\\mu_i),\\sigma_i^2)\\) \\(=1,\\dots,M\\) \\(h_{M+j,t}\\sim N(\\phi_ih_{M+j,t-1},\\sigma_{M+j}^2)\\) \\(j=1,\\dots,r\\).","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Specify prior on Sigma — specify_prior_sigma","text":"Kastner, G. (2019). Sparse Bayesian Time-Varying Covariance Estimation Many Dimensions Journal Econometrics, 210(1), 98--115, doi:10.1016/j.jeconom.2018.11.007 Kastner, G., Frühwirth-Schnatter, S., Lopes, H.F. (2017). Efficient Bayesian Inference Multivariate Factor Stochastic Volatility Models. Journal Computational Graphical Statistics, 26(4), 905--917, doi:10.1080/10618600.2017.1322091 .","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify prior on Sigma — specify_prior_sigma","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # examples with stochastic volatility (heteroscedasticity) ----------------- # factor-decomposition with 2 factors and colwise normal-gamma prior on the loadings sigma_factor_cng_sv <- specify_prior_sigma(data = data, type = \"factor\", factor_factors = 2L, factor_priorfacloadtype = \"colwiseng\", factor_heteroskedastic = TRUE) #>  #> Since argument 'type' is specified with 'factor', all arguments starting with 'cholesky_' are being ignored.  # cholesky-decomposition with Dirichlet-Laplace prior on U sigma_cholesky_dl_sv <- specify_prior_sigma(data = data, type = \"cholesky\", cholesky_U_prior = \"DL\", cholesky_DL_a = 0.5, cholesky_heteroscedastic = TRUE) #>  #> Since argument 'type' is specified with 'cholesky', all arguments starting with 'factor_' are being ignored.  # examples without stochastic volatility (homoscedasticity) ---------------- # factor-decomposition with 2 factors and colwise normal-gamma prior on the loadings sigma_factor_cng <- specify_prior_sigma(data = data, type = \"factor\", factor_factors = 2L, factor_priorfacloadtype = \"colwiseng\", factor_heteroskedastic = FALSE, factor_priorhomoskedastic = matrix(c(0.5,0.5), ncol(data), 2)) #>  #> Since argument 'type' is specified with 'factor', all arguments starting with 'cholesky_' are being ignored. #>  #> Cannot do deep factor_interweaving if (some) factor_factors are homoskedastic. Setting 'factor_interweaving' to 3.  # cholesky-decomposition with Horseshoe prior on U sigma_cholesky_dl <- specify_prior_sigma(data = data, type = \"cholesky\", cholesky_U_prior = \"HS\", cholesky_heteroscedastic = FALSE) #>  #> Since argument 'type' is specified with 'cholesky', all arguments starting with 'factor_' are being ignored. #>  #> Argument 'cholesky_priorhomoscedastic' not specified. Setting both shape and rate of inverse gamma prior equal to 0.01.  # \\donttest{ # Estimate model with your prior configuration of choice mod <- bvar(data, prior_sigma = sigma_factor_cng_sv, quiet = TRUE) # }"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/stable_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Stable posterior draws — stable_bvar","title":"Stable posterior draws — stable_bvar","text":"stable_bvar() detects discards posterior draws bayesianVARs_bvar object fulfill stability condition: VAR(p) model considered stable eigenvalues companion form matrix lie inside unit circle.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/stable_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stable posterior draws — stable_bvar","text":"","code":"stable_bvar(object, quiet = FALSE)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/stable_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stable posterior draws — stable_bvar","text":"object bayesianVARs_bvar object obtained via bvar(). quiet logical indicating whether informative output omitted.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/stable_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stable posterior draws — stable_bvar","text":"object type bayesianVARs_bvar.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/stable_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stable posterior draws — stable_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Discard \"unstable\" draws stable_mod <- stable_bvar(mod) #>  #> Original 'bayesianVARs_bvar' object consists of 1000 posterior draws. #>  #> Detected 475 unstable draws. #>  #> Remaining draws: 525 !"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","title":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","text":"Extract replace parts bayesianVARs_coef object.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","text":"","code":"# S3 method for bayesianVARs_coef [(x, i, j, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","text":"x object type bayesianVARs_coef. indices j indices ... indices","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","text":"object type bayesianVARs_coef.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Extract coefficients, which are of class bayesianVARs_coef phi <- coef(mod) phi[1,1,1] #> [1] 0.2644423 #> attr(,\"class\") #> [1] \"bayesianVARs_coef\"  \"bayesianVARs_draws\""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","title":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","text":"Extract replace parts bayesianVARs_draws object.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","text":"","code":"# S3 method for bayesianVARs_draws [(x, i, j, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","text":"x object type bayesianVARs_draws. indices j indices ... indices","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","text":"object type bayesianVARs_draws.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_draws.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Extract coefficients, which are of class bayesianVARs_draws phi <- coef(mod) phi[1,1,1] #> [1] 0.2662653 #> attr(,\"class\") #> [1] \"bayesianVARs_coef\"  \"bayesianVARs_draws\""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","title":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","text":"Summary method bayesianVARs_bvar objects.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","text":"","code":"# S3 method for bayesianVARs_bvar summary(object, quantiles = c(0.025, 0.25, 0.5, 0.75, 0.975), ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","text":"object bayesianVARs_bvar object obtained via bvar(). quantiles numeric vector quantiles compute. ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","text":"object type summary.bayesianVARs_bvar.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate model mod <- bvar(data, quiet = TRUE)  # Summary sum <- summary(mod)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","title":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","text":"Summary statistics bayesianVARs posterior draws.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","text":"","code":"# S3 method for bayesianVARs_draws summary(object, quantiles = c(0.25, 0.5, 0.75), ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","text":"object object class bayesianVARs_draws usually obtained extractors like coef.bayesianVARs_bvar() vcov.bayesianVARs_bvar(). quantiles vector quantiles evaluate. ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","text":"list object class bayesianVARs_draws_summary holding mean: Vector matrix containing posterior mean. sd: Vector matrix containing posterior standard deviation . quantiles: Array containing posterior quantiles.","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_draws.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")] # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Extract posterior draws of VAR coefficients bvar_coefs <- coef(mod)  # Compute summary statistics summary_stats <- summary(bvar_coefs)  # Compute summary statistics of VAR coefficients without using coef() summary_stats <- summary(mod$PHI)  # Test which list elements of 'mod' are of class 'bayesianVARs_draws'. names(mod)[sapply(names(mod), function(x) inherits(mod[[x]], \"bayesianVARs_draws\"))] #> [1] \"PHI\"     \"U\"       \"logvar\"  \"sv_para\" \"facload\" \"fac\""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","title":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","text":"Summary method bayesianVARs_predict objects.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","text":"","code":"# S3 method for bayesianVARs_predict summary(object, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","text":"object bayesianVARs_predict object obtained via predict.bayesianVARs_bvar(). ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","text":"summary.bayesianVARs_predict object.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Split data in train and test train <- data[1:(nrow(data)-4),] test <- data[-c(1:(nrow(data)-4)),]  # Estimate model using train data only mod <- bvar(train, quiet = TRUE)  # Simulate from 1-step ahead posterior predictive predictions <- predict(mod, ahead = 1L) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  597 stable posterior draws remaining for prediction! summary(predictions) #>  #> Prediction quantiles: #> , , GDPC1 #>  #>          t+1 #> 5%  -0.06623 #> 50% -0.02089 #> 95%  0.02220 #>  #> , , CPIAUCSL #>  #>           t+1 #> 5%  -0.019613 #> 50% -0.008145 #> 95%  0.003785 #>  #> , , FEDFUNDS #>  #>           t+1 #> 5%  -0.022382 #> 50% -0.003965 #> 95%  0.013119 #>"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/usmacro_growth.html","id":null,"dir":"Reference","previous_headings":"","what":"Data from the US-economy — usmacro_growth","title":"Data from the US-economy — usmacro_growth","text":"21 selected quarterly time-series 1953:Q1 2021:Q2. FRED-QD data base (McCracken Ng, 2021). Release date 2021-07. Data transformed interpreted growth-rates (first log-differences exception interest rates, already growth rates).","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/usmacro_growth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data from the US-economy — usmacro_growth","text":"","code":"usmacro_growth"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/usmacro_growth.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data from the US-economy — usmacro_growth","text":"matrix 247 rows 21 columns.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/usmacro_growth.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data from the US-economy — usmacro_growth","text":"Raw (untransformed) data available https://research.stlouisfed.org/econ/mccracken/fred-databases/, https://files.stlouisfed.org/files/htdocs/fred-md/quarterly/2021-07.csv.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/usmacro_growth.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Data from the US-economy — usmacro_growth","text":"McCracken, M. W. Ng, S. (2021). FRED-QD: Quarterly Database Macroeconomic Research, Review, Federal Reserve Bank St. Louis, 103(1), 1--44, doi:10.20955/r.103.1-44 .","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/vcov.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","title":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","text":"Returns posterior draws possibly time-varying variance-covariance matrix VAR estimated via bvar(). Returns full paths sv_keep=\"\" calling bvar(). Otherwise, draws variance-covariance matrix last observation returned, .","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/vcov.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","text":"","code":"# S3 method for bayesianVARs_bvar vcov(object, t = seq_len(nrow(object$logvar)), ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/vcov.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","text":"object object class bayesianVARs_bvar obtained via bvar(). t Vector indicating points time extracted, defaults . ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/vcov.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","text":"array class bayesianVARs_draws dimension \\(T \\times M   \\times M \\times draws\\), \\(T\\) number observations, \\(M\\) number time-series \\(draws\\) number stored posterior draws.","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/vcov.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")] # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Extract posterior draws of the variance-covariance matrix bvar_vcov <- vcov(mod)"},{"path":"https://luisgruber.github.io/bayesianVARs/news/index.html","id":"bayesianvars-013","dir":"Changelog","previous_headings":"","what":"bayesianVARs 0.1.3","title":"bayesianVARs 0.1.3","text":"CRAN release: 2024-06-25 bugfix concerning VAR factor structure errors homoscedastic factors.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/news/index.html","id":"bayesianvars-012","dir":"Changelog","previous_headings":"","what":"bayesianVARs 0.1.2","title":"bayesianVARs 0.1.2","text":"CRAN release: 2024-01-20 Added minimum version factorstochvol Imports field DESCRIPTION file order avoid unnecessary building errors. Thanks Sergey Fedorov pointing . vcov.bayesianVARs_bvar method now can specified specific time-points. bugfix cpp function constructs variance-covariance matrices. Cholesky structure errors specified, exported functions vcov, predict fitted affected.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/news/index.html","id":"bayesianvars-011","dir":"Changelog","previous_headings":"","what":"bayesianVARs 0.1.1","title":"bayesianVARs 0.1.1","text":"CRAN release: 2024-01-17 Fixed clang-UBSAN issue. Fixed undefined figure references vignette.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/news/index.html","id":"bayesianvars-010","dir":"Changelog","previous_headings":"","what":"bayesianVARs 0.1.0","title":"bayesianVARs 0.1.0","text":"CRAN release: 2024-01-13 Initial CRAN submission.","code":""}]
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R package version 0.1.3.9000, https://luisgruber.github.io/bayesianVARs/, https://github.com/luisgruber/bayesianVARs.","code":"@Manual{,   title = {bayesianVARs: MCMC Estimation of Bayesian Vectorautoregressions},   author = {Luis Gruber},   year = {2024},   note = {R package version 0.1.3.9000,     https://luisgruber.github.io/bayesianVARs/},   url = {https://github.com/luisgruber/bayesianVARs}, }"},{"path":"https://luisgruber.github.io/bayesianVARs/index.html","id":"bayesianvars-","dir":"","previous_headings":"","what":"MCMC Estimation of Bayesian Vectorautoregressions","title":"MCMC Estimation of Bayesian Vectorautoregressions","text":"Estimation Bayesian vectorautoregressions /without stochastic volatility. Implements several modern hierarchical shrinkage priors, amongst Dirichlet-Laplace prior (DL), hierarchical Minnesota prior (HM), Horseshoe prior (HS), normal-gamma prior (NG), R2R^2-induced-Dirichlet-decomposition prior (R2D2) stochastic search variable selection prior (SSVS). Concerning error-term, user can either specify order-invariant factor structure order-variant cholesky structure.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MCMC Estimation of Bayesian Vectorautoregressions","text":"Install CRAN version: Install latest development version directly GitHub:","code":"install.packages(\"bayesianVARs\") devtools::install_github(\"luisgruber/bayesianVARs\")"},{"path":"https://luisgruber.github.io/bayesianVARs/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"MCMC Estimation of Bayesian Vectorautoregressions","text":"main workhorse conduct Bayesian inference vectorautoregression models package function bvar(). features: Prediction, plotting, extraction model parameters extraction fitted values usual generic functions predict(), plot(), coef(), vcov() fitted(). Configure prior distributions helper functions specify_prior_phi() specify_prior_sigma().","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/index.html","id":"demonstration","dir":"","previous_headings":"","what":"Demonstration","title":"MCMC Estimation of Bayesian Vectorautoregressions","text":"","code":"set.seed(537) # load package library(bayesianVARs)  # Load data train_data <-100 * usmacro_growth[1:237,c(\"GDPC1\", \"PCECC96\", \"GPDIC1\", \"AWHMAN\", \"GDPCTPI\", \"CES2000000008x\", \"FEDFUNDS\", \"GS10\", \"EXUSUKx\", \"S&P 500\")] test_data <-100 * usmacro_growth[238:241,c(\"GDPC1\", \"PCECC96\", \"GPDIC1\", \"AWHMAN\", \"GDPCTPI\", \"CES2000000008x\", \"FEDFUNDS\", \"GS10\", \"EXUSUKx\", \"S&P 500\")]                                     # Estimate model using default prior settings mod <- bvar(train_data, lags = 2L, draws = 2000, burnin = 1000, sv_keep = \"all\")  # Out of sample prediction and log-predictive-likelihood evaluation pred <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test_data)  # Visualize in-sample fit plus out-of-sample prediction intervals plot(mod, predictions = pred)"},{"path":"https://luisgruber.github.io/bayesianVARs/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"MCMC Estimation of Bayesian Vectorautoregressions","text":"bayesianVARs - Shrinkage Priors Bayesian Vectorautoregressions R","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"bvar simulates joint posterior distribution parameters latent variables returns posterior draws.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"","code":"bvar(   data,   lags = 1L,   draws = 1000L,   burnin = 1000L,   thin = 1L,   prior_intercept = 100,   prior_phi = specify_prior_phi(data = data, lags = lags, prior = \"HS\"),   prior_sigma = specify_prior_sigma(data = data, type = \"factor\", quiet = TRUE),   sv_keep = \"last\",   quiet = FALSE,   startvals = list(),   expert = list() )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"data Data matrix (can time series object). \\(M\\) columns assumed contain single time-series length \\(T\\). lags Integer indicating order VAR, .e. number lags dependent variables included predictors. draws single integer indicating number draws burnin burnin single integer indicating number draws discarded burnin thin single integer. Every \\(thin\\)th draw stored. Default thin=1L. prior_intercept Either prior_intercept=FALSE constant term (intercept) included. numeric vector length \\(M\\) indicating (fixed) prior variances constant term. single number recycled accordingly. Default prior_intercept=100. prior_phi bayesianVARs_prior_phi object specifying prior reduced form VAR coefficients. Best use constructor specify_prior_phi. prior_sigma bayesianVARs_prior_sigma object specifying prior variance-covariance matrix VAR. Best use constructor specify_prior_sigma. sv_keep String equal \"\" \"last\". case sv_keep = \"last\", default, draws last log-variance \\(h_T\\) stored. quiet logical value indicating whether information progress sampling displayed sampling (default TRUE). startvals optional list starting values. expert optional list expert settings.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"object type bayesianVARs_bvar, list containing following objects: PHI: bayesianVARs_coef object, array, containing posterior draws VAR coefficients (including intercept). U: bayesianVARs_draws object, matrix, containing posterior draws contemporaneous coefficients (cholesky decomposition sigma specified). logvar: bayesianVARs_draws object containing log-variance draws. sv_para: baysesianVARs_draws object containing posterior draws stochastic volatility related parameters. phi_hyperparameter: matrix containing posterior draws hyperparameters conditional normal prior VAR coefficients. u_hyperparameter: matrix containing posterior draws hyperparameters conditional normal prior U (cholesky decomposition sigma specified). bench: Numerical indicating average time took generate one single draw joint posterior distribution parameters. V_prior: array containing posterior draws variances conditional normal prior VAR coefficients. facload: bayesianVARs_draws object, array, containing draws posterior distribution factor loadings matrix (factor decomposition sigma specified). fac: bayesianVARs_draws object, array, containing factor draws posterior distribution (factor decomposition sigma specified). Y: Matrix containing dependent variables used estimation. X matrix containing lagged values dependent variables, .e. covariates. lags: Integer indicating lag order VAR. intercept: Logical indicating whether constant term included. heteroscedastic logical indicating whether heteroscedasticity assumed. Yraw: Matrix containing dependent variables, including initial 'lags' observations. Traw: Integer indicating total number observations. sigma_type: Character specifying decomposition variance-covariance matrix. datamat: Matrix containing 'Y' 'X'. config: List containing information configuration parameters.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"VAR(p) model following form: \\( \\boldsymbol{y}^\\prime_t = \\boldsymbol{\\iota}^\\prime + \\boldsymbol{x}^\\prime_t\\boldsymbol{\\Phi} + \\boldsymbol{\\epsilon}^\\prime_t\\), \\(\\boldsymbol{y}_t\\) \\(M\\)-dimensional vector dependent variables \\(\\boldsymbol{\\epsilon}_t\\) error term dimension. \\(\\boldsymbol{x}_t\\) \\(K=pM\\)-dimensional vector containing lagged/past values dependent variables \\(\\boldsymbol{y}_{t-l}\\) \\(l=1,\\dots,p\\) \\(\\boldsymbol{\\iota}\\) constant term (intercept) dimension \\(M\\times 1\\). reduced-form coefficient matrix \\(\\boldsymbol{\\Phi}\\) dimension \\(K \\times M\\). bvar offers two different specifications errors: user can choose factor stochastic volatility structure cholesky stochastic volatility structure. cases disturbances \\(\\boldsymbol{\\epsilon}_t\\) assumed follow \\(M\\)-dimensional multivariate normal distribution zero mean variance-covariance matrix \\(\\boldsymbol{\\Sigma}_t\\). case cholesky specification \\(\\boldsymbol{\\Sigma}_t = \\boldsymbol{U}^{\\prime -1} \\boldsymbol{D}_t \\boldsymbol{U}^{-1}\\), \\(\\boldsymbol{U}^{-1}\\) upper unitriangular (ones diagonal). diagonal matrix \\(\\boldsymbol{D}_t\\) depends upon latent log-variances, .e. \\(\\boldsymbol{D}_t=diag(exp(h_{1t}),\\dots, exp(h_{Mt})\\). log-variances follow priori independent autoregressive processes \\(h_{}\\sim N(\\mu_i + \\phi_i(h_{,t-1}-\\mu_i),\\sigma_i^2)\\) \\(=1,\\dots,M\\). case factor structure, \\(\\boldsymbol{\\Sigma}_t = \\boldsymbol{\\Lambda} \\boldsymbol{V}_t \\boldsymbol{\\Lambda}^\\prime + \\boldsymbol{G}_t\\). diagonal matrices \\(\\boldsymbol{V}_t\\) \\(\\boldsymbol{G}_t\\) depend upon latent log-variances, .e. \\(\\boldsymbol{G}_t=diag(exp(h_{1t}),\\dots, exp(h_{Mt})\\) \\(\\boldsymbol{V}_t=diag(exp(h_{M+1,t}),\\dots, exp(h_{M+r,t})\\). log-variances follow priori independent autoregressive processes \\(h_{}\\sim N(\\mu_i + \\phi_i(h_{,t-1}-\\mu_i),\\sigma_i^2)\\) \\(=1,\\dots,M\\) \\(h_{M+j,t}\\sim N(\\phi_ih_{M+j,t-1},\\sigma_{M+j}^2)\\) \\(j=1,\\dots,r\\).","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"mcmc-algorithm","dir":"Reference","previous_headings":"","what":"MCMC algorithm","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"sample efficiently reduced-form VAR coefficients assuming factor structure errors, equation per equation algorithm Kastner & Huber (2020) implemented. parameters latent variables associated factor-structure sampled using package factorstochvol-package's function update_fsv callable C-level . sample efficiently reduced-form VAR coefficients, assuming cholesky-structure errors, corrected triangular algorithm Carriero et al. (2021) implemented. SV parameters latent variables sampled using package stochvol's update_fast_sv function. precision parameters, .e. free -diagonal elements \\(\\boldsymbol{U}\\), sampled Cogley Sargent (2005).","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"Gruber, L. Kastner, G. (2023). Forecasting macroeconomic data Bayesian VARs: Sparse dense? depends! arXiv:2206.04902. Kastner, G. Huber, F. Sparse (2020). Bayesian vector autoregressions huge dimensions. Journal Forecasting. 39, 1142–1165, doi:10.1002/.2680 . Kastner, G. (2019). Sparse Bayesian Time-Varying Covariance Estimation Many Dimensions Journal Econometrics, 210(1), 98–115, doi:10.1016/j.jeconom.2018.11.007 . Carriero, . Chan, J.  Clark, T. E. Marcellino, M. (2021). Corrigendum “Large Bayesian vector autoregressions stochastic volatility non-conjugate priors” [J. Econometrics 212 (1) (2019) 137–154]. Journal Econometrics, doi:10.1016/j.jeconom.2021.11.010 . Cogley, S. Sargent, T. (2005). Drifts volatilities: monetary policies outcomes post WWII US. Review Economic Dynamics, 8, 262–302, doi:10.1016/j.red.2004.10.009 . Hosszejni, D. Kastner, G. (2021). Modeling Univariate Multivariate Stochastic Volatility R stochvol factorstochvol. Journal Statistical Software, 100, 1–-34. doi:10.18637/jss.v100.i12 .","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions — bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Plot plot(mod)   # Summary summary(mod) #>  #> Posterior median of reduced-form coefficients: #>                 GDPC1 CPIAUCSL   FEDFUNDS #> GDPC1.l1     0.234251 0.006679  1.613e-02 #> CPIAUCSL.l1 -0.052490 0.609837 -2.106e-03 #> FEDFUNDS.l1  0.008550 0.038264  1.001e+00 #> intercept    0.005904 0.001249 -6.616e-05 #>  #> Posterior interquartile range of of reduced-form coefficients: #>                GDPC1  CPIAUCSL  FEDFUNDS #> GDPC1.l1    0.103406 0.0310374 0.0224336 #> CPIAUCSL.l1 0.112489 0.0972602 0.0136029 #> FEDFUNDS.l1 0.017590 0.0146875 0.0069129 #> intercept   0.001103 0.0007549 0.0001674"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract VAR coefficients — coef","title":"Extract VAR coefficients — coef","text":"Extracts posterior draws VAR coefficients VAR model estimated bvar().","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract VAR coefficients — coef","text":"","code":"# S3 method for class 'bayesianVARs_bvar' coef(object, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract VAR coefficients — coef","text":"object bayesianVARs_bvar object obtained bvar(). ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract VAR coefficients — coef","text":"Returns numeric array dimension \\(M \\times K \\times draws\\), M number time-series, K number covariates per equation (including intercept) draws number stored posterior draws.","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract VAR coefficients — coef","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Extract posterior draws of VAR coefficients bvar_coefs <- coef(mod)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/fitted.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","title":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","text":"Simulates fitted/predicted (-sample) values estimated VAR model.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/fitted.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","text":"","code":"# S3 method for class 'bayesianVARs_bvar' fitted(object, error_term = TRUE, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/fitted.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","text":"object bayesianVARs_bvar object estimated via bvar(). error_term logical indicating whether include error term . ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/fitted.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","text":"object class bayesianVARs_fitted.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/fitted.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate fitted/predicted historical values for an estimated VAR model — fitted.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Simulate predicted historical values including the error term. pred <- fitted(mod, error_term = TRUE)  # Simulate fitted historical values not including the error term. fit <- fitted(mod, error_term = FALSE)  # Visualize plot(pred)  plot(fit)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/my_gig.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw from generalized inverse Gaussian — my_gig","title":"Draw from generalized inverse Gaussian — my_gig","text":"Vectorized version rgig","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/my_gig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw from generalized inverse Gaussian — my_gig","text":"","code":"my_gig(n, lambda, chi, psi)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/my_gig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw from generalized inverse Gaussian — my_gig","text":"n single integer indicating number draws generate. lambda vector shape parameters. chi vector shape/scale parameters. Must nonnegative positive lambdas positive else. psi vector shape/scale parameters. Must nonnegative negative lambdas positive else.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/my_gig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw from generalized inverse Gaussian — my_gig","text":"Matrix dimension c(n,m), m maximum length lambda, psi chi.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/my_gig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw from generalized inverse Gaussian — my_gig","text":"","code":"gigsamples <- my_gig(2, c(1,1), c(1,1), c(1,1))"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"Pairwise visualization --sample posterior predictive densities.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"","code":"# S3 method for class 'bayesianVARs_predict' pairs(x, vars, ahead, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"x object class bayesianVARs_predict obtained via predict.bayesianVARs_bvar(). vars Integer vector (coercible ) indicating variables plot. ahead Integer vector (coercible ) indicating step ahead plot. max(ahead) must smaller equal dim(x$predictions)[1]. ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"Returns x invisibly.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"Note bayesianVARs_predict can also used withing plot.bayesianVARs_bvar().","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/pairs_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise visualization of out-of-sample posterior predictive densities. — pairs_predict","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Simulate from posterior predictive predictions <- predict(mod, ahead = 1:3) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  526 stable posterior draws remaining for prediction!  # Visualize pairs(predictions, vars = 1:3, ahead = 1:3)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","title":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","text":"Visualization -sample fit. Can also used display prediction intervals future values.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","text":"","code":"# S3 method for class 'bayesianVARs_bvar' plot(   x,   predictions = NULL,   quantiles = c(0.05, 0.5, 0.95),   dates = NULL,   n_col = 1,   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","text":"x object class bayesianVARs_bvar obtained via bvar(). predictions Optional array sample predictions, e.g. obtained via predict.bayesianVARs_bvar(). quantiles numeric vector indicating quantiles plot. dates optional vector dates labelling x-axis. default values NULL; case, axis labeled numbers. n_col integer indicating number columns use plotting. ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","text":"Returns x invisibly.","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot method for bayesianVARs_bvar — plot.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Simulate from posterior predictive predictions <- predict(mod, ahead = 1:3) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  428 stable posterior draws remaining for prediction!  # Visualize plot(mod, predictions = predictions)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_fitted.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","title":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","text":"Visualization -sample fit estimated VAR.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_fitted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","text":"","code":"# S3 method for class 'bayesianVARs_fitted' plot(   x,   dates = NULL,   vars = \"all\",   quantiles = c(0.05, 0.5, 0.95),   n_col = 1L,   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_fitted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","text":"x bayesianVARs_fitted object. dates optional vector dates labelling x-axis. default values NULL; case, axis labeled numbers. vars character vector containing names variables visualized. default \"\" indicating fit variables visualized. quantiles numeric vector indicating quantiles plot. n_col integer indicating number columns use plotting. ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_fitted.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","text":"returns x invisibly","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_fitted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualization of in-sample fit of an estimated VAR. — plot.bayesianVARs_fitted","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Simulate predicted historical values including the error term. pred <- fitted(mod, error_term = TRUE)  # Visualize plot(pred)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Fan chart — plot.bayesianVARs_predict","title":"Fan chart — plot.bayesianVARs_predict","text":"Visualization (--sample) predictive distribution.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fan chart — plot.bayesianVARs_predict","text":"","code":"# S3 method for class 'bayesianVARs_predict' plot(   x,   dates = NULL,   vars = \"all\",   ahead = NULL,   quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),   n_col = 1L,   first_obs = 1L,   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fan chart — plot.bayesianVARs_predict","text":"x object type bayesianVARs_predict obtained via predict.bayesianVARs_bvar(). dates optional vector dates labeling x-axis. default values NULL; case, axis labeled numbers. vars character vector containing names variables visualized. default \"\" indicating variables visualized. ahead Integer vector (coercible ) indicating step ahead plot. max(ahead) must smaller equal dim(x$predictions)[1]. quantiles numeric vector indicating quantiles plot. n_col integer indicating number columns use plotting. first_obs integer indicating first observation used plotting. ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fan chart — plot.bayesianVARs_predict","text":"Returns x invisibly!","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/plot.bayesianVARs_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fan chart — plot.bayesianVARs_predict","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Simulate from posterior predictive predictions <- predict(mod, ahead = 1:3) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  433 stable posterior draws remaining for prediction!  # Visualize plot(predictions, vars = 1:3, ahead = 1:3)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/posterior_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","title":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","text":"Posterior heatmaps VAR coefficients variance-covariance matrices","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/posterior_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","text":"","code":"posterior_heatmap(   x,   FUN,   ...,   colorbar = TRUE,   xlabels = NULL,   ylabels = NULL,   add_numbers = FALSE,   zlim = NULL,   colspace = NULL,   main = \"\",   cex.axis = 0.75,   cex.colbar = 1,   cex.numbers = 1,   asp = NULL )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/posterior_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","text":"x array dimension \\(\\times b \\times draws\\), \\(  \\times b\\) dimension parameter visualize draws number posterior draws. FUN summary function applied margins c(1,2) x. E.g. \"median\", \"mean\", \"IQR\", \"sd\" \"var\". apply(x, 1:2, FUN, ...) must return matrix! ... optional arguments FUN. colorbar logical indicating whether display colorbar . Default TRUE. xlabels ylabels=NULL, default, indicates names dependent variables displayed. ylabels=\"\" indicates ylabels displayed. ylabels xlabels=NULL, default, indicates labels covariables (lagged values dependent variables) displayed. xlabels=\"lags\" indicates lags marked. xlabels=\"\" indicates ylabels displayed. add_numbers logical. add_numbers=TRUE, default indicates actual values summary displayed. zlim numeric vector length two indicating minimum maximum values colors plotted. default range determined maximum absolute values selected summary. colspace Optional argument. main main title plot. cex.axis magnification used y-axis annotation relative current setting cex. cex.colbar magnification used colorbar annotation relative current setting cex. cex.numbers magnification used actual values (add_numbers=TRUE) relative current setting cex. asp aspect ratio.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/posterior_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","text":"Returns x invisibly.","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/posterior_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Posterior heatmaps for VAR coefficients or variance-covariance matrices — posterior_heatmap","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(100*data, sv_keep = \"all\", quiet = TRUE)  # Extract posterior draws of VAR coefficients phi_post <- coef(mod)  # Visualize posterior median of VAR coefficients posterior_heatmap(phi_post, median)   # Extract posterior draws of variance-covariance matrices (for each point in time) sigma_post <- vcov(mod) # Visualize posterior interquartile-range of variance-covariance matrix of the first observation posterior_heatmap(sigma_post[1,,,], IQR)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/predict.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","title":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","text":"Simulates (--sample) predictive density Bayesian VARs estimated via bvar() computes log predictive likelhoods ex-post observed data supplied.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/predict.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","text":"","code":"# S3 method for class 'bayesianVARs_bvar' predict(   object,   ahead = 1L,   each = 1L,   stable = TRUE,   simulate_predictive = TRUE,   LPL = FALSE,   Y_obs = NA,   LPL_VoI = NA,   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/predict.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","text":"object bayesianVARs_bvar object, obtained bvar(). ahead Integer vector (coercible ), indicating number steps ahead predict. Single integer (coercible ) indicating often drawn posterior predictive distribution draw stored MCMC sampling. stable logical indicating whether consider draws posterior fulfill 'stable' criterion. Default TRUE. simulate_predictive logical, indicating whether posterior predictive distribution simulated. LPL logical indicating whether ahead-step-ahead log predictive likelihoods computed. LPL=TRUE, Y_obs specified. Y_obs Data matrix observed values computation log predictive likelihood. ncol(object$Yraw) columns assumed contain single time-series length length(ahead). LPL_VoI either integer vector character vector column-names indicating subgroup time-series object$Yraw joint log predictive likelihood shall computed. ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/predict.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","text":"Object class bayesianVARs_predict, list may contain following elements: predictions array dimensions c(length(ahead), ncol(object$Yraw), * dim(object$PHI)[3]) containing simulations predictive density (simulate_predictive=TRUE). LPL vector length length(ahead) containing log-predictive-likelihoods (taking account joint distribution variables) (LPL=TRUE). LPL_univariate matrix dimension c(length(ahead), ncol(object$Yraw) containing marginalized univariate log-predictive-likelihoods series (LPL=TRUE). LPL_VoI vector length length(ahead) containing log-predictive-likelihoods subset variables (LPL=TRUE LPL_VoI != NA). Yraw matrix containing data used estimation VAR. LPL_draws matrix containing simulations log-predictive-likelihood (LPL=TRUE). PL_univariate_draws array containing simulations univariate predictive-likelihoods (LPL=TRUE). LPL_sub_draws matrix containing simulations log-predictive-likelihood subset variables (LPL=TRUE LPL_VoI != NA).","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/predict.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict method for Bayesian VARs — predict.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Split data in train and test train <- data[1:(nrow(data)-4),] test <- data[-c(1:(nrow(data)-4)),]  # Estimate model using train data only mod <- bvar(train, quiet = TRUE)  # Simulate from 1-step to 4-steps ahead posterior predictive and compute # log-predictive-likelihoods predictions <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  643 stable posterior draws remaining for prediction!  # Summary summary(predictions) #>  #> LPL: #>   t+1   t+2   t+3   t+4  #> 2.718 8.556 8.419 6.221  #>  #> Marginal univariate LPLs: #>      GDPC1 CPIAUCSL FEDFUNDS #> t+1 -1.226   0.5816    3.554 #> t+2  2.479   2.7464    3.248 #> t+3  2.622   2.5796    3.081 #> t+4  2.797   0.6113    2.936 #>  #> Prediction quantiles: #> , , GDPC1 #>  #>          t+1       t+2       t+3       t+4 #> 5%  -0.07047 -0.039599 -0.023225 -0.017323 #> 50% -0.02556 -0.004664  0.003999  0.005908 #> 95%  0.01933  0.030542  0.035271  0.027565 #>  #> , , CPIAUCSL #>  #>           t+1       t+2       t+3        t+4 #> 5%  -0.019737 -0.019905 -0.018104 -0.0142483 #> 50% -0.008343 -0.005330 -0.003288 -0.0008071 #> 95%  0.003750  0.007712  0.009475  0.0122323 #>  #> , , FEDFUNDS #>  #>           t+1       t+2       t+3       t+4 #> 5%  -0.021391 -0.031018 -0.045467 -0.050481 #> 50% -0.004621 -0.005998 -0.006405 -0.007743 #> 95%  0.012474  0.021421  0.032884  0.032841 #>   # Visualize via fan-charts plot(predictions)   # \\donttest{ # In order to evaluate the joint predictive density of a subset of the # variables (variables of interest), consider specifying 'LPL_VoI': predictions <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test, LPL_VoI = c(\"GDPC1\",\"FEDFUNDS\")) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  643 stable posterior draws remaining for prediction! predictions$LPL_VoI #>      t+1      t+2      t+3      t+4  #> 2.345726 5.790619 5.785145 5.795078  # }"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Pretty printing of a bvar object — print.bayesianVARs_bvar","title":"Pretty printing of a bvar object — print.bayesianVARs_bvar","text":"Pretty printing bvar object","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pretty printing of a bvar object — print.bayesianVARs_bvar","text":"","code":"# S3 method for class 'bayesianVARs_bvar' print(x, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pretty printing of a bvar object — print.bayesianVARs_bvar","text":"x Object class bayesianVARs_bvar, usually resulting call bvar(). ... Ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pretty printing of a bvar object — print.bayesianVARs_bvar","text":"Returns x invisibly.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pretty printing of a bvar object — print.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Print model mod #>  #> Fitted bayesianVARs_bvar object with #>   -       3 series #>   -       1 lag(s) #>   -     246 used observations #>   -     247 total observations #>   -    1000 MCMC draws #>   -       1 thinning #>   -    1000 burn-in #>"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","title":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","text":"Print method bayesianVARs_predict objects.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","text":"","code":"# S3 method for class 'bayesianVARs_predict' print(x, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","text":"x bayesianVARs_predict object obtained via predict.bayesianVARs_bvar(). ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","text":"Returns x invisibly.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.bayesianVARs_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print method for bayesianVARs_predict objects — print.bayesianVARs_predict","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Split data in train and test train <- data[1:(nrow(data)-4),] test <- data[-c(1:(nrow(data)-4)),]  # Estimate model using train data only mod <- bvar(train, quiet = TRUE)  # Simulate from 1-step ahead posterior predictive predictions <- predict(mod, ahead = 1L) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  723 stable posterior draws remaining for prediction! print(predictions) #>  #> Generic functions for bayesianVARs_predict objects: #>   - summary.bayesianVARs_predict(), #>   - pairs.bayesianVARs_predict(), #>   - plot.bayesianVARs_predict() (alias for pairs.bayesianVARs_predict())."},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","title":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","text":"Print method summary.bayesianVARs_bvar objects.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","text":"","code":"# S3 method for class 'summary.bayesianVARs_bvar' print(x, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","text":"x summary.bayesianVARs_bvar object obtained via summary.bayesianVARs_bvar(). ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","text":"Returns x invisibly!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print method for summary.bayesianVARs_bvar objects — print.summary.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate model mod <- bvar(data, quiet = TRUE)  # Print summary summary(mod) #>  #> Posterior median of reduced-form coefficients: #>                 GDPC1 CPIAUCSL   FEDFUNDS #> GDPC1.l1     0.212473 0.006487  1.697e-02 #> CPIAUCSL.l1 -0.032907 0.607315 -2.100e-03 #> FEDFUNDS.l1  0.004907 0.037169  1.001e+00 #> intercept    0.006025 0.001392 -5.789e-05 #>  #> Posterior interquartile range of of reduced-form coefficients: #>               GDPC1  CPIAUCSL  FEDFUNDS #> GDPC1.l1    0.15327 0.0289077 0.0267194 #> CPIAUCSL.l1 0.09095 0.0971672 0.0140055 #> FEDFUNDS.l1 0.01419 0.0148861 0.0075846 #> intercept   0.00157 0.0007101 0.0001965"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","title":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","text":"Print method summary.bayesianVARs_predict objects.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","text":"","code":"# S3 method for class 'summary.bayesianVARs_predict' print(x, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","text":"x summary.bayesianVARs_predict object obtained via summary.bayesianVARs_predict(). ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","text":"Returns x invisibly.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/print.summary.bayesianVARs_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print method for summary.bayesianVARs_predict objects — print.summary.bayesianVARs_predict","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Split data in train and test train <- data[1:(nrow(data)-4),] test <- data[-c(1:(nrow(data)-4)),]  # Estimate model using train data only mod <- bvar(train, quiet = TRUE)  # Simulate from 1-step ahead posterior predictive predictions <- predict(mod, ahead = 1L) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  596 stable posterior draws remaining for prediction! sum <- summary(predictions) print(sum) #>  #> Prediction quantiles: #> , , GDPC1 #>  #>          t+1 #> 5%  -0.07121 #> 50% -0.01811 #> 95%  0.02637 #>  #> , , CPIAUCSL #>  #>           t+1 #> 5%  -0.021118 #> 50% -0.007914 #> 95%  0.003595 #>  #> , , FEDFUNDS #>  #>           t+1 #> 5%  -0.020831 #> 50% -0.004230 #> 95%  0.008696 #>"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify prior on PHI — specify_prior_phi","title":"Specify prior on PHI — specify_prior_phi","text":"Configures prior PHI, matrix reduced-form VAR coefficients.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify prior on PHI — specify_prior_phi","text":"","code":"specify_prior_phi(   data = NULL,   M = ncol(data),   lags = 1L,   prior = \"HS\",   priormean = 0,   PHI_tol = 1e-18,   DL_a = \"1/K\",   DL_tol = 0,   R2D2_a = 0.1,   R2D2_b = 0.5,   R2D2_tol = 0,   NG_a = 0.1,   NG_b = 1,   NG_c = 1,   NG_tol = 0,   SSVS_c0 = 0.01,   SSVS_c1 = 100,   SSVS_semiautomatic = TRUE,   SSVS_p = 0.5,   HMP_lambda1 = c(0.01, 0.01),   HMP_lambda2 = c(0.01, 0.01),   normal_sds = 10,   global_grouping = \"global\",   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify prior on PHI — specify_prior_phi","text":"data Optional. Data matrix (can time series object). \\(M\\) columns assumed contain single time-series length \\(T\\). M positive integer indicating number time-series VAR. lags positive integer indicating order VAR, .e. number lags dependent variables included predictors. prior character, one \"HS\", \"R2D2\", \"NG\", \"DL\", \"SSVS\", \"HMP\" \"normal\". priormean real numbers indicating prior means VAR coefficients. One single number means prior mean -lag coefficients w.r.t. first lag equals priormean 0 else. vector length M means prior mean -lag coefficients w.r.t. first lag equals priormean 0 else. priormean matrix dimension c(lags*M,M), \\(M\\) columns assumed contain lags*M prior means VAR coefficients respective VAR equations. PHI_tol Minimum number absolute value VAR coefficient draw can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. DL_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. argument global_grouping specifies e.g. k groups, DL_a can numeric vector length k elements indicate shrinkage group.  matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. DL_a specified prior=\"DL\". DL_tol Minimum number parameter draw one shrinking parameters Dirichlet Laplace prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. DL_tol specified prior=\"DL\". R2D2_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. argument global_grouping specifies e.g. k groups, R2D2_a can numeric vector length k elements indicate shrinkage group.  matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. R2D2_a specified prior=\"R2D2\". R2D2_b (Single) positive real number. value indicates shape parameter inverse gamma prior (semi-)global scales. argument global_grouping specifies e.g. k groups, NG_b can numeric vector length k elements determine shape parameter group. R2D2_b specified prior=\"R2D2\". R2D2_tol Minimum number parameter draw one shrinking parameters R2D2 prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. R2D2_tol specified prior=\"R2D2\". NG_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. argument global_grouping specifies e.g. k groups, NG_a can numeric vector length k elements indicate shrinkage group.  matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. NG_a specified prior=\"NG\". NG_b (Single) positive real number. value indicates shape parameter inverse gamma prior (semi-)global scales. argument global_grouping specifies e.g. k groups, NG_b can numeric vector length k elements determine shape parameter group. NG_b specified prior=\"NG\". NG_c (Single) positive real number. value indicates scale parameter inverse gamma prior (semi-)global scales. argument global_grouping specifies e.g. k groups, NG_c can numeric vector length k elements determine scale parameter group. Expert option set scale parameter proportional NG_a. E.g. case discrete hyperprior NG_a chosen, desired proportion say 0.2 achieved setting NG_c=\"0.2a\" (character input!). NG_c specified prior=\"NG\". NG_tol Minimum number parameter draw one shrinking parameters normal-gamma prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. NG_tol specified prior=\"NG\". SSVS_c0 single positive number indicating (unscaled) standard deviation spike component. SSVS_c0 specified prior=\"SSVS\". \\(SSVS_{c0} \\ll SSVS_{c1}\\)! SSVS_c0 specified prior=\"SSVS\". SSVS_c1 single positive number indicating (unscaled) standard deviation slab component. SSVS_c0 specified prior=\"SSVS\". \\(SSVS_{c0} \\ll SSVS_{c1}\\)! SSVS_semiautomatic logical. SSVS_semiautomatic=TRUE SSVS_c0 SSVS_c1 scaled variances posterior PHI FLAT conjugate (dependent Normal-Wishart prior). SSVS_semiautomatic specified prior=\"SSVS\". SSVS_p Either single positive number range (0,1) indicating (fixed) prior inclusion probability coefficient. numeric vector length 2 positive entries indicating shape parameters Beta distribution. case Beta hyperprior placed prior inclusion probability. SSVS_p specified prior=\"SSVS\". HMP_lambda1 numeric vector length 2. entries must positive. first indicates shape second rate Gamma hyperprior -lag coefficients. HMP_lambda1 specified prior=\"HMP\". HMP_lambda2 numeric vector length 2. entries must positive. first indicates shape second rate Gamma hyperprior cross-lag coefficients. HMP_lambda2 specified prior=\"HMP\". normal_sds numeric vector length \\(n\\), \\(n = lags M^2\\) number VAR coefficients (excluding intercept), indicating prior variances. single number recycled accordingly! Must positive. normal_sds specified prior=\"normal\". global_grouping One \"global\", \"equation-wise\", \"covariate-wise\", \"olcl-lagwise\" \"fol\" indicating sub-groups semi-global(-local) modifications HS, R2D2, NG, DL SSVS prior. Works also user-specified indicator matrix dimension c(lags*M,M).  relevant prior=\"HS\", prior=\"DL\", prior=\"R2D2\", prior=\"NG\" prior=\"SSVS\". ... use!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify prior on PHI — specify_prior_phi","text":"baysianVARs_prior_phi-object.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify prior on PHI — specify_prior_phi","text":"details concerning prior-elicitation VARs please see Gruber & Kastner (2023). Currently one can choose six hierarchical shrinkage priors normal prior: prior=\"HS\" stands Horseshoe-prior, prior=\"R2D2 R\\(^2\\)-induced-Dirichlet-decompostion-prior, prior=\"NG\" normal-gamma-prior, prior=\"DL\" Dirichlet-Laplace-prior, prior=\"SSVS\" stochastic-search-variable-selection-prior, prior=\"HMP\" semi-hierarchical Minnesota prior prior=normal normal-prior. Semi-global shrinkage, .e. group-specific shrinkage pre-specified subgroups coefficients, can achieved argument global_grouping.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Specify prior on PHI — specify_prior_phi","text":"Gruber, L. Kastner, G. (2023). Forecasting macroeconomic data Bayesian VARs: Sparse dense? depends! arXiv:2206.04902.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_phi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify prior on PHI — specify_prior_phi","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Horseshoe prior for a VAR(2) phi_hs <- specify_prior_phi(data = data, lags = 2L ,prior = \"HS\")  # Semi-global-local Horseshoe prior for a VAR(2) with semi-global shrinkage parameters for # cross-lag and own-lag coefficients in each lag phi_hs_sg <- specify_prior_phi(data = data, lags = 2L, prior = \"HS\", global_grouping = \"olcl-lagwise\")  # Semi-global-local Horseshoe prior for a VAR(2) with equation-wise shrinkage # construct indicator matrix for equation-wise shrinkage semi_global_mat <- matrix(1:ncol(data), 2*ncol(data), ncol(data), byrow = TRUE) phi_hs_ew <- specify_prior_phi(data = data, lags = 2L, prior = \"HS\", global_grouping = semi_global_mat) # (for equation-wise shrinkage one can also use 'global_grouping = \"equation-wise\"')  # \\donttest{ # Estimate model with your prior configuration of choice mod <- bvar(data, lags = 2L, prior_phi = phi_hs_sg, quiet = TRUE) # }"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify prior on Sigma — specify_prior_sigma","title":"Specify prior on Sigma — specify_prior_sigma","text":"Configures prior variance-covariance VAR.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify prior on Sigma — specify_prior_sigma","text":"","code":"specify_prior_sigma(   data = NULL,   M = ncol(data),   type = c(\"factor\", \"cholesky\"),   factor_factors = 1L,   factor_restrict = c(\"none\", \"upper\"),   factor_priorfacloadtype = c(\"rowwiseng\", \"colwiseng\", \"normal\"),   factor_priorfacload = 0.1,   factor_facloadtol = 1e-18,   factor_priorng = c(1, 1),   factor_priormu = c(0, 10),   factor_priorphiidi = c(10, 3),   factor_priorphifac = c(10, 3),   factor_priorsigmaidi = 1,   factor_priorsigmafac = 1,   factor_priorh0idi = \"stationary\",   factor_priorh0fac = \"stationary\",   factor_heteroskedastic = TRUE,   factor_priorhomoskedastic = NA,   factor_interweaving = 4,   cholesky_U_prior = c(\"HS\", \"DL\", \"R2D2\", \"NG\", \"SSVS\", \"normal\", \"HMP\"),   cholesky_U_tol = 1e-18,   cholesky_heteroscedastic = TRUE,   cholesky_priormu = c(0, 100),   cholesky_priorphi = c(20, 1.5),   cholesky_priorsigma2 = c(0.5, 0.5),   cholesky_priorh0 = \"stationary\",   cholesky_priorhomoscedastic = as.numeric(NA),   cholesky_DL_a = \"1/n\",   cholesky_DL_tol = 0,   cholesky_R2D2_a = 0.4,   cholesky_R2D2_b = 0.5,   cholesky_R2D2_tol = 0,   cholesky_NG_a = 0.5,   cholesky_NG_b = 0.5,   cholesky_NG_c = 0.5,   cholesky_NG_tol = 0,   cholesky_SSVS_c0 = 0.001,   cholesky_SSVS_c1 = 1,   cholesky_SSVS_p = 0.5,   cholesky_HMP_lambda3 = c(0.01, 0.01),   cholesky_normal_sds = 10,   expert_sv_offset = 0,   quiet = FALSE,   ... )"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify prior on Sigma — specify_prior_sigma","text":"data Optional. Data matrix (can time series object). \\(M\\) columns assumed contain single time-series length \\(T\\). M positive integer indicating number time-series VAR. type character, one \"factor\" (default) \"cholesky\", indicating decomposition applied covariance-matrix. factor_factors Number latent factors estimated. required type=\"factor\". factor_restrict Either \"upper\" \"none\", indicating whether factor loadings matrix restricted zeros diagonal (\"upper\") whether elements estimated data (\"none\"). Setting restrict \"upper\" often stabilizes MCMC estimation can important identifying factor loadings matrix, however, generally strong prior assumption. Setting restrict \"none\" usually preferred option identification factor loadings matrix less concern covariance estimation prediction goal. required type=\"factor\". factor_priorfacloadtype Can \"normal\", \"rowwiseng\", \"colwiseng\". required type=\"factor\". \"normal\": Normal prior. value priorfacload interpreted standard deviations Gaussian prior distributions factor loadings. \"rowwiseng\": Row-wise Normal-Gamma prior. value priorfacload interpreted shrinkage parameter . \"colwiseng\": Column-wise Normal-Gamma prior. value priorfacload interpreted shrinkage parameter . details please see Kastner (2019). factor_priorfacload Either matrix dimensions M times factor_factors positive elements single number (recycled accordingly). required type=\"factor\". meaning factor_priorfacload depends setting factor_priorfacloadtype explained . factor_facloadtol Minimum number absolute value factor loadings draw can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. required type=\"factor\". factor_priorng Two-element vector positive entries indicating Normal-Gamma prior's hyperhyperparameters c d (cf. Kastner (2019)). required type=\"factor\". factor_priormu Vector length 2 denoting prior mean standard deviation unconditional levels idiosyncratic log variance processes. required type=\"factor\". factor_priorphiidi Vector length 2, indicating shape parameters Beta prior distributions transformed parameters (phi+1)/2, phi denotes persistence idiosyncratic log variances. required type=\"factor\". factor_priorphifac Vector length 2, indicating shape parameters Beta prior distributions transformed parameters (phi+1)/2, phi denotes persistence factor log variances. required type=\"factor\". factor_priorsigmaidi Vector length M containing prior volatilities log variances. factor_priorsigmaidi exactly one element, recycled idiosyncratic log variances. required type=\"factor\". factor_priorsigmafac Vector length factor_factors containing prior volatilities log variances. factor_priorsigmafac exactly one element, recycled factor log variances. required type=\"factor\". factor_priorh0idi Vector length 1 M, containing information Gaussian prior initial idiosyncratic log variances. required type=\"factor\". element factor_priorh0idi nonnegative number, conditional prior corresponding initial log variance h0 assumed Gaussian mean 0 standard deviation factor_priorh0idi times \\(sigma\\). element factor_priorh0idi string 'stationary', prior corresponding initial log volatility taken stationary distribution, .e. h0 assumed Gaussian mean 0 variance \\(sigma^2/(1-phi^2)\\). factor_priorh0fac Vector length 1 factor_factors, containing information Gaussian prior initial factor log variances. required type=\"factor\". element factor_priorh0fac nonnegative number, conditional prior corresponding initial log variance h0 assumed Gaussian mean 0 standard deviation factor_priorh0fac times \\(sigma\\). element factor_priorh0fac string 'stationary', prior corresponding initial log volatility taken stationary distribution, .e. h0 assumed Gaussian mean 0 variance \\(sigma^2/(1-phi^2)\\). factor_heteroskedastic Vector length 1, 2, M +  factor_factors, containing logical values indicating whether time-varying (factor_heteroskedastic = TRUE) constant (factor_heteroskedastic =   FALSE) variance estimated. factor_heteroskedastic length 2 recycled accordingly, whereby first element used idiosyncratic variances second element used factor variances. required type=\"factor\". factor_priorhomoskedastic used least one element factor_heteroskedastic set FALSE. case, factor_priorhomoskedastic must matrix positive entries dimension c(M, 2). Values column 1 interpreted shape values column 2 interpreted rate parameter corresponding inverse gamma prior distribution idiosyncratic variances. required type=\"factor\". factor_interweaving following values interweaving factor loadings accepted (required type=\"factor\"): 0: interweaving. 1: Shallow interweaving diagonal entries. 2: Deep interweaving diagonal entries. 3: Shallow interweaving largest absolute entries column. 4: Deep interweaving largest absolute entries column. details please see Kastner et al. (2017). value 4 highly recommended default. cholesky_U_prior character, one \"HS\", \"R2D2\", \"NG\", \"DL\", \"SSVS\", \"HMP\" \"normal\". required type=\"cholesky\". cholesky_U_tol Minimum number absolute value free -diagonal element \\(U\\)-draw can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. required type=\"cholesky\". cholesky_heteroscedastic single logical indicating whether time-varying (cholesky_heteroscedastic = TRUE) constant (cholesky_heteroscedastic = FALSE) variance estimated. required type=\"cholesky\". cholesky_priormu Vector length 2 denoting prior mean standard deviation unconditional levels log variance processes. required type=\"cholesky\". cholesky_priorphi Vector length 2, indicating shape parameters Beta prior distributions transformed parameters (phi+1)/2, phi denotes persistence log variances. required type=\"cholesky\". cholesky_priorsigma2 Vector length 2, indicating shape rate Gamma prior distributions variance log variance processes. (Currently one global setting \\(M\\) processes supported). required type=\"cholesky\". cholesky_priorh0 Vector length 1 M, containing information Gaussian prior initial idiosyncratic log variances. required type=\"cholesky\". element cholesky_priorh0 nonnegative number, conditional prior corresponding initial log variance h0 assumed Gaussian mean 0 standard deviation cholesky_priorh0 times \\(sigma\\). element cholesky_priorh0 string 'stationary', prior corresponding initial log volatility taken stationary distribution, .e. h0 assumed Gaussian mean 0 variance \\(sigma^2/(1-phi^2)\\). cholesky_priorhomoscedastic used cholesky_heteroscedastic=FALSE. case, cholesky_priorhomoscedastic must matrix positive entries dimension c(M, 2). Values column 1 interpreted shape values column 2 interpreted scale parameter corresponding inverse gamma prior distribution variances. required type=\"cholesky\". cholesky_DL_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. cholesky_DL_a specified cholesky_U_prior=\"DL\". cholesky_DL_tol Minimum number parameter draw one shrinking parameters Dirichlet Laplace prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. DL_tol specified cholesky_U_prior=\"DL\". cholesky_R2D2_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. cholesky_R2D2_a specified cholesky_U_prior=\"R2D2\". cholesky_R2D2_b single positive number, greater values indicate heavier regularization. cholesky_R2D2_b specified cholesky_U_prior=\"R2D2\". cholesky_R2D2_tol Minimum number parameter draw one shrinking parameters R2D2 prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. cholesky_R2D2_tol specified cholesky_U_prior=\"R2D2\". cholesky_NG_a (Single) positive real number. value interpreted concentration parameter local scales. Smaller values enforce heavier shrinkage. matrix dimension c(s,2) specifies discrete hyperprior, first column contains s support points second column contains associated prior probabilities. cholesky_NG_a specified cholesky_U_prior=\"NG\". cholesky_NG_b (Single) positive real number. value indicates shape parameter inverse gamma prior global scales. cholesky_NG_b specified cholesky_U_prior=\"NG\". cholesky_NG_c (Single) positive real number. value indicates scale parameter inverse gamma prior global scales. Expert option set scale parameter proportional NG_a. E.g. case discrete hyperprior NG_a chosen, desired proportion say 0.2 achieved setting NG_c=\"0.2a\" (character input!). cholesky_NG_c specified cholesky_U_prior=\"NG\". cholesky_NG_tol Minimum number parameter draw one shrinking parameters normal-gamma prior can take. Prevents numerical issues can appear strong shrinkage enforced chosen greater zero. cholesky_NG_tol specified cholesky_U_prior=\"NG\". cholesky_SSVS_c0 single positive number indicating (unscaled) standard deviation spike component. cholesky_SSVS_c0 specified choleksy_U_prior=\"SSVS\". \\(SSVS_{c0}   \\ll SSVS_{c1}\\)! cholesky_SSVS_c1 single positive number indicating (unscaled) standard deviation slab component. cholesky_SSVS_c1 specified choleksy_U_prior=\"SSVS\". \\(SSVS_{c0} \\ll SSVS_{c1}\\)! cholesky_SSVS_p Either single positive number range (0,1) indicating (fixed) prior inclusion probability coefficient. numeric vector length 2 positive entries indicating shape parameters Beta distribution. case Beta hyperprior placed prior inclusion probability. cholesky_SSVS_p specified choleksy_U_prior=\"SSVS\". cholesky_HMP_lambda3 numeric vector length 2. entries must positive. first indicates shape second rate Gamma hyperprior contemporaneous coefficients. cholesky_HMP_lambda3 specified choleksy_U_prior=\"HMP\". cholesky_normal_sds numeric vector length \\(\\frac{M^2-M}{2}\\), indicating prior variances free -diagonal elements \\(U\\). single number recycled accordingly! Must positive. cholesky_normal_sds specified choleksy_U_prior=\"normal\". expert_sv_offset ... use! quiet logical indicating whether informative output omitted. ... use!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify prior on Sigma — specify_prior_sigma","text":"Object class bayesianVARs_prior_sigma.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify prior on Sigma — specify_prior_sigma","text":"bvar offers two different specifications errors: user can choose factor stochastic volatility structure cholesky stochastic volatility structure. cases disturbances \\(\\boldsymbol{\\epsilon}_t\\) assumed follow \\(M\\)-dimensional multivariate normal distribution zero mean variance-covariance matrix \\(\\boldsymbol{\\Sigma}_t\\). case cholesky specification \\(\\boldsymbol{\\Sigma}_t = \\boldsymbol{U}^{\\prime -1} \\boldsymbol{D}_t \\boldsymbol{U}^{-1}\\), \\(\\boldsymbol{U}^{-1}\\) upper unitriangular (ones diagonal). diagonal matrix \\(\\boldsymbol{D}_t\\) depends upon latent log-variances, .e. \\(\\boldsymbol{D}_t=diag(exp(h_{1t}),\\dots, exp(h_{Mt})\\). log-variances follow priori independent autoregressive processes \\(h_{}\\sim N(\\mu_i + \\phi_i(h_{,t-1}-\\mu_i),\\sigma_i^2)\\) \\(=1,\\dots,M\\). case factor structure, \\(\\boldsymbol{\\Sigma}_t = \\boldsymbol{\\Lambda} \\boldsymbol{V}_t \\boldsymbol{\\Lambda}^\\prime + \\boldsymbol{G}_t\\). diagonal matrices \\(\\boldsymbol{V}_t\\) \\(\\boldsymbol{G}_t\\) depend upon latent log-variances, .e. \\(\\boldsymbol{G}_t=diag(exp(h_{1t}),\\dots, exp(h_{Mt})\\) \\(\\boldsymbol{V}_t=diag(exp(h_{M+1,t}),\\dots, exp(h_{M+r,t})\\). log-variances follow priori independent autoregressive processes \\(h_{}\\sim N(\\mu_i + \\phi_i(h_{,t-1}-\\mu_i),\\sigma_i^2)\\) \\(=1,\\dots,M\\) \\(h_{M+j,t}\\sim N(\\phi_ih_{M+j,t-1},\\sigma_{M+j}^2)\\) \\(j=1,\\dots,r\\).","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Specify prior on Sigma — specify_prior_sigma","text":"Kastner, G. (2019). Sparse Bayesian Time-Varying Covariance Estimation Many Dimensions Journal Econometrics, 210(1), 98–115, doi:10.1016/j.jeconom.2018.11.007 Kastner, G., Frühwirth-Schnatter, S., Lopes, H.F. (2017). Efficient Bayesian Inference Multivariate Factor Stochastic Volatility Models. Journal Computational Graphical Statistics, 26(4), 905–917, doi:10.1080/10618600.2017.1322091 .","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/specify_prior_sigma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify prior on Sigma — specify_prior_sigma","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # examples with stochastic volatility (heteroscedasticity) ----------------- # factor-decomposition with 2 factors and colwise normal-gamma prior on the loadings sigma_factor_cng_sv <- specify_prior_sigma(data = data, type = \"factor\", factor_factors = 2L, factor_priorfacloadtype = \"colwiseng\", factor_heteroskedastic = TRUE) #>  #> Since argument 'type' is specified with 'factor', all arguments starting with 'cholesky_' are being ignored.  # cholesky-decomposition with Dirichlet-Laplace prior on U sigma_cholesky_dl_sv <- specify_prior_sigma(data = data, type = \"cholesky\", cholesky_U_prior = \"DL\", cholesky_DL_a = 0.5, cholesky_heteroscedastic = TRUE) #>  #> Since argument 'type' is specified with 'cholesky', all arguments starting with 'factor_' are being ignored.  # examples without stochastic volatility (homoscedasticity) ---------------- # factor-decomposition with 2 factors and colwise normal-gamma prior on the loadings sigma_factor_cng <- specify_prior_sigma(data = data, type = \"factor\", factor_factors = 2L, factor_priorfacloadtype = \"colwiseng\", factor_heteroskedastic = FALSE, factor_priorhomoskedastic = matrix(c(0.5,0.5), ncol(data), 2)) #>  #> Since argument 'type' is specified with 'factor', all arguments starting with 'cholesky_' are being ignored. #>  #> Cannot do deep factor_interweaving if (some) factor_factors are homoskedastic. Setting 'factor_interweaving' to 3.  # cholesky-decomposition with Horseshoe prior on U sigma_cholesky_dl <- specify_prior_sigma(data = data, type = \"cholesky\", cholesky_U_prior = \"HS\", cholesky_heteroscedastic = FALSE) #>  #> Since argument 'type' is specified with 'cholesky', all arguments starting with 'factor_' are being ignored. #>  #> Argument 'cholesky_priorhomoscedastic' not specified. Setting both shape and rate of inverse gamma prior equal to 0.01.  # \\donttest{ # Estimate model with your prior configuration of choice mod <- bvar(data, prior_sigma = sigma_factor_cng_sv, quiet = TRUE) # }"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/stable_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Stable posterior draws — stable_bvar","title":"Stable posterior draws — stable_bvar","text":"stable_bvar() detects discards posterior draws bayesianVARs_bvar object fulfill stability condition: VAR(p) model considered stable eigenvalues companion form matrix lie inside unit circle.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/stable_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stable posterior draws — stable_bvar","text":"","code":"stable_bvar(object, quiet = FALSE)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/stable_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stable posterior draws — stable_bvar","text":"object bayesianVARs_bvar object obtained via bvar(). quiet logical indicating whether informative output omitted.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/stable_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stable posterior draws — stable_bvar","text":"object type bayesianVARs_bvar.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/stable_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stable posterior draws — stable_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Discard \"unstable\" draws stable_mod <- stable_bvar(mod) #>  #> Original 'bayesianVARs_bvar' object consists of 1000 posterior draws. #>  #> Detected 540 unstable draws. #>  #> Remaining draws: 460 !"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","title":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","text":"Extract replace parts bayesianVARs_coef object.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","text":"","code":"# S3 method for class 'bayesianVARs_coef' x[i, j, ...]"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","text":"x object type bayesianVARs_coef. indices j indices ... indices","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","text":"object type bayesianVARs_coef.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or Replace Parts of a bayesianVARs_coef object — [.bayesianVARs_coef","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Extract coefficients, which are of class bayesianVARs_coef phi <- coef(mod) phi[1,1,1] #> [1] 0.1591042 #> attr(,\"class\") #> [1] \"bayesianVARs_coef\"  \"bayesianVARs_draws\""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","title":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","text":"Extract replace parts bayesianVARs_draws object.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","text":"","code":"# S3 method for class 'bayesianVARs_draws' x[i, j, ...]"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","text":"x object type bayesianVARs_draws. indices j indices ... indices","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","text":"object type bayesianVARs_draws.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/sub-.bayesianVARs_draws.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or Replace Parts of a bayesianVARs_draws object — [.bayesianVARs_draws","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Extract coefficients, which are of class bayesianVARs_draws phi <- coef(mod) phi[1,1,1] #> [1] 0.3361933 #> attr(,\"class\") #> [1] \"bayesianVARs_coef\"  \"bayesianVARs_draws\""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","title":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","text":"Summary method bayesianVARs_bvar objects.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","text":"","code":"# S3 method for class 'bayesianVARs_bvar' summary(object, quantiles = c(0.025, 0.25, 0.5, 0.75, 0.975), ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","text":"object bayesianVARs_bvar object obtained via bvar(). quantiles numeric vector quantiles compute. ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","text":"object type summary.bayesianVARs_bvar.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Estimate model mod <- bvar(data, quiet = TRUE)  # Summary sum <- summary(mod)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","title":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","text":"Summary statistics bayesianVARs posterior draws.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","text":"","code":"# S3 method for class 'bayesianVARs_draws' summary(object, quantiles = c(0.25, 0.5, 0.75), ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","text":"object object class bayesianVARs_draws usually obtained extractors like coef.bayesianVARs_bvar() vcov.bayesianVARs_bvar(). quantiles vector quantiles evaluate. ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","text":"list object class bayesianVARs_draws_summary holding mean: Vector matrix containing posterior mean. sd: Vector matrix containing posterior standard deviation . quantiles: Array containing posterior quantiles.","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_draws.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary statistics for bayesianVARs posterior draws. — summary.bayesianVARs_draws","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")] # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Extract posterior draws of VAR coefficients bvar_coefs <- coef(mod)  # Compute summary statistics summary_stats <- summary(bvar_coefs)  # Compute summary statistics of VAR coefficients without using coef() summary_stats <- summary(mod$PHI)  # Test which list elements of 'mod' are of class 'bayesianVARs_draws'. names(mod)[sapply(names(mod), function(x) inherits(mod[[x]], \"bayesianVARs_draws\"))] #> [1] \"PHI\"     \"U\"       \"logvar\"  \"sv_para\" \"facload\" \"fac\""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","title":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","text":"Summary method bayesianVARs_predict objects.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","text":"","code":"# S3 method for class 'bayesianVARs_predict' summary(object, ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","text":"object bayesianVARs_predict object obtained via predict.bayesianVARs_bvar(). ... Currently ignored!","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","text":"summary.bayesianVARs_predict object.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/summary.bayesianVARs_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for bayesianVARs_predict objects — summary.bayesianVARs_predict","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")]  # Split data in train and test train <- data[1:(nrow(data)-4),] test <- data[-c(1:(nrow(data)-4)),]  # Estimate model using train data only mod <- bvar(train, quiet = TRUE)  # Simulate from 1-step ahead posterior predictive predictions <- predict(mod, ahead = 1L) #> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior #>           draws, that do not fulfill the stable criterion. #>  #>  509 stable posterior draws remaining for prediction! summary(predictions) #>  #> Prediction quantiles: #> , , GDPC1 #>  #>          t+1 #> 5%  -0.07418 #> 50% -0.02086 #> 95%  0.03223 #>  #> , , CPIAUCSL #>  #>           t+1 #> 5%  -0.020029 #> 50% -0.007975 #> 95%  0.003687 #>  #> , , FEDFUNDS #>  #>           t+1 #> 5%  -0.020075 #> 50% -0.003383 #> 95%  0.016755 #>"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/usmacro_growth.html","id":null,"dir":"Reference","previous_headings":"","what":"Data from the US-economy — usmacro_growth","title":"Data from the US-economy — usmacro_growth","text":"21 selected quarterly time-series 1953:Q1 2021:Q2. FRED-QD data base (McCracken Ng, 2021). Release date 2021-07. Data transformed interpreted growth-rates (first log-differences exception interest rates, already growth rates).","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/usmacro_growth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data from the US-economy — usmacro_growth","text":"","code":"usmacro_growth"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/usmacro_growth.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data from the US-economy — usmacro_growth","text":"matrix 247 rows 21 columns.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/usmacro_growth.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data from the US-economy — usmacro_growth","text":"Raw (untransformed) data available https://research.stlouisfed.org/econ/mccracken/fred-databases/, https://files.stlouisfed.org/files/htdocs/fred-md/quarterly/2021-07.csv.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/usmacro_growth.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Data from the US-economy — usmacro_growth","text":"McCracken, M. W. Ng, S. (2021). FRED-QD: Quarterly Database Macroeconomic Research, Review, Federal Reserve Bank St. Louis, 103(1), 1–44, doi:10.20955/r.103.1-44 .","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/vcov.bayesianVARs_bvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","title":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","text":"Returns posterior draws possibly time-varying variance-covariance matrix VAR estimated via bvar(). Returns full paths sv_keep=\"\" calling bvar(). Otherwise, draws variance-covariance matrix last observation returned, .","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/vcov.bayesianVARs_bvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","text":"","code":"# S3 method for class 'bayesianVARs_bvar' vcov(object, t = seq_len(nrow(object$logvar)), ...)"},{"path":"https://luisgruber.github.io/bayesianVARs/reference/vcov.bayesianVARs_bvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","text":"object object class bayesianVARs_bvar obtained via bvar(). t Vector indicating points time extracted, defaults . ... Currently ignored.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/reference/vcov.bayesianVARs_bvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","text":"array class bayesianVARs_draws dimension \\(T \\times M   \\times M \\times draws\\), \\(T\\) number observations, \\(M\\) number time-series \\(draws\\) number stored posterior draws.","code":""},{"path":[]},{"path":"https://luisgruber.github.io/bayesianVARs/reference/vcov.bayesianVARs_bvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model — vcov.bayesianVARs_bvar","text":"","code":"# Access a subset of the usmacro_growth dataset data <- usmacro_growth[,c(\"GDPC1\", \"CPIAUCSL\", \"FEDFUNDS\")] # Estimate a model mod <- bvar(data, sv_keep = \"all\", quiet = TRUE)  # Extract posterior draws of the variance-covariance matrix bvar_vcov <- vcov(mod)"},{"path":"https://luisgruber.github.io/bayesianVARs/news/index.html","id":"bayesianvars-development-version","dir":"Changelog","previous_headings":"","what":"bayesianVARs (development version)","title":"bayesianVARs (development version)","text":"consistency functions, now prior_intercept bvar() specifies standard deviations instead variances.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/news/index.html","id":"bayesianvars-013","dir":"Changelog","previous_headings":"","what":"bayesianVARs 0.1.3","title":"bayesianVARs 0.1.3","text":"CRAN release: 2024-06-25 bugfix concerning VAR factor structure errors homoscedastic factors.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/news/index.html","id":"bayesianvars-012","dir":"Changelog","previous_headings":"","what":"bayesianVARs 0.1.2","title":"bayesianVARs 0.1.2","text":"CRAN release: 2024-01-20 Added minimum version factorstochvol Imports field DESCRIPTION file order avoid unnecessary building errors. Thanks Sergey Fedorov pointing . vcov.bayesianVARs_bvar method now can specified specific time-points. bugfix cpp function constructs variance-covariance matrices. Cholesky structure errors specified, exported functions vcov, predict fitted affected.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/news/index.html","id":"bayesianvars-011","dir":"Changelog","previous_headings":"","what":"bayesianVARs 0.1.1","title":"bayesianVARs 0.1.1","text":"CRAN release: 2024-01-17 Fixed clang-UBSAN issue. Fixed undefined figure references vignette.","code":""},{"path":"https://luisgruber.github.io/bayesianVARs/news/index.html","id":"bayesianvars-010","dir":"Changelog","previous_headings":"","what":"bayesianVARs 0.1.0","title":"bayesianVARs 0.1.0","text":"CRAN release: 2024-01-13 Initial CRAN submission.","code":""}]
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