From 95e202f3d719eb894d5cc5e1e11988c1d4b56341 Mon Sep 17 00:00:00 2001 From: cmjt Date: Tue, 15 Oct 2024 14:41:01 +1300 Subject: [PATCH] update docs --- docs/_quarto.yml | 10 +- docs/data.html | 191 +- .../figure-html/unnamed-chunk-11-1.png | Bin 0 -> 26501 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 0 -> 103996 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 0 -> 17305 bytes .../figure-html/unnamed-chunk-16-1.png | Bin 0 -> 41924 bytes .../figure-html/unnamed-chunk-19-1.png | Bin 0 -> 19859 bytes .../figure-html/unnamed-chunk-23-1.png | Bin 0 -> 22712 bytes .../figure-html/unnamed-chunk-24-1.png | Bin 0 -> 64389 bytes .../figure-html/unnamed-chunk-26-1.png | Bin 0 -> 64150 bytes .../figure-html/unnamed-chunk-28-1.png | Bin 0 -> 59778 bytes .../figure-html/unnamed-chunk-30-1.png | Bin 0 -> 102884 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 0 -> 18531 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 0 -> 18769 bytes docs/index.html | 479 +++++ docs/intro.html | 380 ++++ 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docs/univariate-hawkes_files/figure-html/unnamed-chunk-2-1.png delete mode 100644 docs/univariate-hawkes_files/figure-html/unnamed-chunk-3-1.png diff --git a/docs/_quarto.yml b/docs/_quarto.yml index b8aa6de..3992732 100644 --- a/docs/_quarto.yml +++ b/docs/_quarto.yml @@ -22,11 +22,11 @@ book: - marked-hawkes.qmd - multi-hawkes.qmd - lgcp.qmd - - lgcp-spatiotemporal.qmd - - lgcp-marked.qmd - - stelfi.qmd - - stelfi-gmrf.qmd - - meshmetrics.qmd + #- lgcp-spatiotemporal.qmd + #- lgcp-marked.qmd + #- stelfi.qmd + #- stelfi-gmrf.qmd + #- meshmetrics.qmd - summary.qmd - data.qmd - references.qmd diff --git a/docs/data.html b/docs/data.html index 694ed0f..ab9ef8f 100644 --- a/docs/data.html +++ b/docs/data.html @@ -264,31 +264,18 @@

-
devtools::load_all("~/Git/stelfi")
-
-
ℹ Loading stelfi
-
-
data(package = "stelfi")$result[, c("Item", "Title")]
+
data(package = "stelfi")$result[, "Item"]
-
      Item             Title
- [1,] "horse_mesh"     ""   
- [2,] "horse_sf"       ""   
- [3,] "iraq_terrorism" ""   
- [4,] "marked"         ""   
- [5,] "multi_hawkes"   ""   
- [6,] "nz_earthquakes" ""   
- [7,] "nz_murders"     ""   
- [8,] "retweets_niwa"  ""   
- [9,] "sasquatch"      ""   
-[10,] "uk_serial"      ""   
-[11,] "xyt"            ""   
+
 [1] "horse_mesh"     "horse_sf"       "iraq_terrorism" "marked"        
+ [5] "multi_hawkes"   "nz_earthquakes" "nz_murders"     "retweets_niwa" 
+ [9] "sasquatch"      "uk_serial"      "xyt"           

7.1 Temporal point pattern data

-
## load the tidyverse packages
-library(tidyverse)
+
## load the tidyverse packages
+library(tidyverse)

retweets_niwa

@@ -301,8 +288,8 @@

retweets_ni

The retweets_niwa dataset contains the retweet timestamps for this tweet.

-
data(retweets_niwa, package = "stelfi")
-head(retweets_niwa)
+
data(retweets_niwa, package = "stelfi")
+head(retweets_niwa)
[1] "2019-02-07 06:50:08 UTC" "2019-02-07 06:50:08 UTC"
 [3] "2019-02-07 06:49:22 UTC" "2019-02-07 06:48:48 UTC"
@@ -310,9 +297,9 @@ 

retweets_ni

-
ggplot(data.frame(time = retweets_niwa), aes(x = time)) + 
-  geom_histogram() + ylab("Retweet frequency") + xlab("") +
-  theme_bw()
+
ggplot(data.frame(time = retweets_niwa), aes(x = time)) + 
+  geom_histogram() + ylab("Retweet frequency") + xlab("") +
+  theme_bw()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
@@ -325,8 +312,8 @@

retweets_ni

uk_serial

Murder UK documents some of the UKs most infamous multiple murderer cases. The uk_serial dataset contains summary information about the documented cases along with approximate timeframes.

-
data("uk_serial", package = "stelfi")
-head(uk_serial)
+
data("uk_serial", package = "stelfi")
+head(uk_serial)
  number_of_kills       years                           name                aka
 1             300 1995 - 1998             Dr. Harold Shipman          Dr. Death
@@ -345,13 +332,13 @@ 

uk_serial

-
uk_serial %>%
-  mutate(time =  paste(date_of_first_kill, "/01", sep='')) %>%
-  mutate(time = as.Date(time, "%m/%Y/%d")) %>%
-  ggplot(aes(x = time)) + 
-  geom_histogram() + 
-  ylab("Frequency of known first kill") + 
-  xlab("") + theme_bw()
+
uk_serial %>%
+  mutate(time =  paste(date_of_first_kill, "/01", sep='')) %>%
+  mutate(time = as.Date(time, "%m/%Y/%d")) %>%
+  ggplot(aes(x = time)) + 
+  geom_histogram() + 
+  ylab("Frequency of known first kill") + 
+  xlab("") + theme_bw()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
@@ -365,22 +352,22 @@

uk_serial7.2 Spatiotemporal point pattern data

Using maps to create sf objects of country boundaries:

-
us <- maps::map("usa", fill = TRUE, plot = FALSE) %>%
-    sf::st_as_sf() %>%
-    sf::st_make_valid()
-nz <- maps::map("nz", fill = TRUE, plot = FALSE) %>%
-    sf::st_as_sf() %>%
-    sf::st_make_valid()
-iraq <- maps::map("world", "Iraq", fill = TRUE, plot = FALSE) %>%
-     sf::st_as_sf() %>%
-    sf::st_make_valid()
+
us <- maps::map("usa", fill = TRUE, plot = FALSE) %>%
+    sf::st_as_sf() %>%
+    sf::st_make_valid()
+nz <- maps::map("nz", fill = TRUE, plot = FALSE) %>%
+    sf::st_as_sf() %>%
+    sf::st_make_valid()
+iraq <- maps::map("world", "Iraq", fill = TRUE, plot = FALSE) %>%
+     sf::st_as_sf() %>%
+    sf::st_make_valid()

sasquatch

The Bigfoot Field Researchers Organization (BFRO) documents Bigfoot (Sasquatch) sightings; some data have been collated and packaged in stelfi assasquatch.

-
data("sasquatch", package = "stelfi")
-sasquatch
+
data("sasquatch", package = "stelfi")
+sasquatch
Simple feature collection with 972 features and 27 fields
 Geometry type: POINT
@@ -413,19 +400,19 @@ 

sasquatch
-
ggplot(sasquatch, aes(x = date)) + geom_histogram(bins = 150) +
-  ylab("Frequency of Sasquatch sightings") + xlab("") +
-  theme_bw()
+
ggplot(sasquatch, aes(x = date)) + geom_histogram(bins = 150) +
+  ylab("Frequency of Sasquatch sightings") + xlab("") +
+  theme_bw()

-
ggplot(sasquatch) +  
-  geom_sf(alpha = 0.3) + 
-  coord_sf() + 
-  geom_sf(data = us, fill = NA) + 
-  theme_classic()
+
ggplot(sasquatch) +  
+  geom_sf(alpha = 0.3) + 
+  coord_sf() + 
+  geom_sf(data = us, fill = NA) + 
+  theme_classic()
Coordinate system already present. Adding new coordinate system, which will
 replace the existing one.
@@ -439,8 +426,8 @@

sasquatchnz_earthquakes

GeoNet Quake Search catalogues New Zealand earthquake occurrence; some of these data have been and packaged in stelfi as nz_earthquakes. In this chapter a Hawkes process is fitted to these data.

-
data("nz_earthquakes", package = "stelfi")
-nz_earthquakes
+
data("nz_earthquakes", package = "stelfi")
+nz_earthquakes
Simple feature collection with 3824 features and 3 fields
 Geometry type: POINT
@@ -465,19 +452,19 @@ 

nz_earthqu

-
ggplot(nz_earthquakes, aes(x = origintime)) + geom_histogram(bins = 100) +
-  ylab("Frequency of earthquakes") + xlab("") +
-  theme_bw()
+
ggplot(nz_earthquakes, aes(x = origintime)) + geom_histogram(bins = 100) +
+  ylab("Frequency of earthquakes") + xlab("") +
+  theme_bw()

-
ggplot(nz_earthquakes) +  
-  geom_sf(alpha = 0.1) + 
-  coord_sf() + 
-  geom_sf(data = nz, fill = NA) +
-  theme_classic()
+
ggplot(nz_earthquakes) +  
+  geom_sf(alpha = 0.1) + 
+  coord_sf() + 
+  geom_sf(data = nz, fill = NA) +
+  theme_classic()
Coordinate system already present. Adding new coordinate system, which will
 replace the existing one.
@@ -491,8 +478,8 @@

nz_earthqu

nz_murders

The Homicide Report documents homicides in New Zealand. The nz_murders dataset contains summary information about the documented cases. In this chapter a spatiotemporal self-exciting model is fitted to these data.

-
data("nz_murders", package = "stelfi")
-nz_murders
+
data("nz_murders", package = "stelfi")
+nz_murders
Simple feature collection with 967 features and 11 fields
 Geometry type: POINT
@@ -539,9 +526,9 @@ 

nz_murders
-
ggplot(nz_murders, aes(x = full_date)) + geom_histogram(bins = 100) +
-  ylab("Frequency of murders") + xlab("") +
-  theme_bw()
+
ggplot(nz_murders, aes(x = full_date)) + geom_histogram(bins = 100) +
+  ylab("Frequency of murders") + xlab("") +
+  theme_bw()
Warning: Removed 8 rows containing non-finite outside the scale range
 (`stat_bin()`).
@@ -558,8 +545,8 @@

nz_murdersiraq_terrorism

The Global Terrorism Database (GTD) documents information on terrorism events worldwide; some of these data have been and packaged in stelfi as iraq_terrorism.

-
data("iraq_terrorism", package = "stelfi")
-iraq_terrorism
+
data("iraq_terrorism", package = "stelfi")
+iraq_terrorism
Simple feature collection with 4208 features and 16 fields
 Geometry type: POINT
@@ -617,22 +604,22 @@ 

iraq_terro

-
iraq_terrorism %>%
-  mutate(date = paste(iday, imonth, iyear, sep = "/")) %>%
-  mutate(date = as.Date(date, "%d/%m/%Y")) %>%
-  ggplot(., aes(x = date)) + geom_histogram(bins = 150) +
-  ylab("Frequency of attacks") + xlab("") +
-  theme_bw()
+
iraq_terrorism %>%
+  mutate(date = paste(iday, imonth, iyear, sep = "/")) %>%
+  mutate(date = as.Date(date, "%d/%m/%Y")) %>%
+  ggplot(., aes(x = date)) + geom_histogram(bins = 150) +
+  ylab("Frequency of attacks") + xlab("") +
+  theme_bw()

-
ggplot(iraq_terrorism) +  
-  geom_sf(alpha = 0.3) + 
-  coord_sf() + 
-  geom_sf(data = iraq, fill = NA) +
-  theme_classic()
+
ggplot(iraq_terrorism) +  
+  geom_sf(alpha = 0.3) + 
+  coord_sf() + 
+  geom_sf(data = iraq, fill = NA) +
+  theme_classic()
Coordinate system already present. Adding new coordinate system, which will
 replace the existing one.
@@ -649,9 +636,9 @@

xyt

In this chapter a log-Gaussian Cox process is fitted to these data and in this chapter a spatiotemporal selfexciting model is fitted.

-
data("xyt", package = "stelfi")
-xyt_sf <- sf::st_as_sf(xyt)
-xyt_sf
+
data("xyt", package = "stelfi")
+xyt_sf <- sf::st_as_sf(xyt)
+xyt_sf
Simple feature collection with 654 features and 1 field
 Geometry type: GEOMETRY
@@ -673,8 +660,8 @@ 

xyt

-
ggplot(xyt_sf) + geom_sf(fill = NA) +
-  theme_void()
+
ggplot(xyt_sf) + geom_sf(fill = NA) +
+  theme_void()

@@ -684,10 +671,10 @@

xyt

marked

In this chapter a marked log-Gaussian Cox process is fitted to these data.

-
data(marked, package = "stelfi")
-marked_sf <- sf::st_as_sf(x = marked,
-                        coords = c("x", "y"))
-marked_sf
+
data(marked, package = "stelfi")
+marked_sf <- sf::st_as_sf(x = marked,
+                        coords = c("x", "y"))
+marked_sf
Simple feature collection with 159 features and 3 fields
 Geometry type: POINT
@@ -709,14 +696,14 @@ 

marked

-
domain <- list(3 * cbind(c(0, 1, 1, 0, 0), c(0, 0, 1, 1, 0))) %>%
-  sf::st_polygon() %>% sf::st_sfc() %>% sf::st_sf(geometry = .)
-
-ggplot(marked_sf, aes(col = m1)) + 
-  geom_sf() + labs(color = "Mark") +
-  scale_color_continuous(type = "viridis") +
-  geom_sf(data = domain, fill = NA, inherit.aes = FALSE) +
-  theme_void()
+
domain <- list(3 * cbind(c(0, 1, 1, 0, 0), c(0, 0, 1, 1, 0))) %>%
+  sf::st_polygon() %>% sf::st_sfc() %>% sf::st_sf(geometry = .)
+
+ggplot(marked_sf, aes(col = m1)) + 
+  geom_sf() + labs(color = "Mark") +
+  scale_color_continuous(type = "viridis") +
+  geom_sf(data = domain, fill = NA, inherit.aes = FALSE) +
+  theme_void()

@@ -726,9 +713,9 @@

marked

horse_mesh

In this chapter we illustrate different geometric metrics of this triangulation.

-
data("horse_mesh", package = "stelfi")
-horse_mesh_sf <- stelfi::mesh_2_sf(horse_mesh)
-horse_mesh_sf
+
data("horse_mesh", package = "stelfi")
+horse_mesh_sf <- stelfi::mesh_2_sf(horse_mesh)
+horse_mesh_sf
Simple feature collection with 396 features and 4 fields
 Geometry type: POLYGON
@@ -750,8 +737,8 @@ 

horse_mesh

-
ggplot(horse_mesh_sf) + geom_sf(fill = NA, col = "black") +
-  theme_void()
+
ggplot(horse_mesh_sf) + geom_sf(fill = NA, col = "black") +
+  theme_void()

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+
+ + +
+ +
+ + + + + +
+ +
+
+

The R package stelfi

+
+ +
+
+ Using stelfi +
+
+ + +
+ +
+
Author
+
+

Charlotte M. Jones-Todd

+
+
+ + + +
+ + +
+ +
+

Preface

+
+
+
+ +
+
+Warning +
+
+
+

This book is a work in progress, please bear with me as I continue to update it.

+

If you have any particular examples you’d like to be included please let me know @ c.jonestodd@auckland.ac.nz

+
+
+
+

Installation

+
+
+
ℹ Loading stelfi
+
+
+

Option 1: stelfi is on CRAN

+
+
install.packages("stelfi")
+
+

Option 2: the development version of stelfi package can be installed from GitHub

+
+
devtools::install_github("cmjt/stelfi")
+
+
+
+
+ +
+
+Note +
+
+
+

To cite stelfi use

+
+
citation("stelfi")
+
+
To cite `stelfi` in pubications use:
+
+  C. M. Jones-Todd, and A. B. M. van Helsdingen ABM (2024), stelfi: An
+  R package for fitting Hawkes and log-Gaussian Cox point process
+  models, Ecology and Evolution, 14, e11005,doi:10.1002/ece3.11005
+
+A BibTeX entry for LaTeX users is
+
+  @Article{,
+    title = {stelfi: An R package for fitting Hawkes and log-Gaussian Cox point process models},
+    author = {Charlotte M. Jones-Todd and Alec B. M. {van Helsdingen}},
+    year = {2024},
+    journal = {Ecology and Evolution},
+    volume = {14},
+    pages = {e11005},
+    doi = {10.1002/ece3.11005},
+  }
+
+
+
+
+ + +
+
+ +
+ + +
+ + + + \ No newline at end of file diff --git a/docs/intro.html b/docs/intro.html new file mode 100644 index 0000000..082234f --- /dev/null +++ b/docs/intro.html @@ -0,0 +1,380 @@ + + + + + + + + + + +The R package stelfi - 1  Introduction + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + + + + +
+ +
+
+

1  Introduction

+
+ + + +
+ + + + +
+ + +
+ +
+
+
+ +
+
+Warning +
+
+
+

This book is a work in progress, please bear with me as I update and fix the issues.

+

If you have any particular examples you’d like to be included please let me know @ c.jonestodd@auckland.ac.nz

+
+
+

The stelfi package fits Hawkes and log-Gaussian Cox Point Process models, with extensions, using Template Model Builder (Kristensen et al. (2016)).

+

Overview

+

A Hawkes process is a self-exciting temporal point process where the occurrence of an event immediately increases the chance of another (see Hawkes (1971)). stelfi also offers functionality to fit self-inhibiting process and a non-homogeneous background rate.

+

A log-Gaussian Cox process is a Poisson point process where the log-intensity is given by a Gaussian random field. stelfi also offers functionality to extend this to a joint likelihood formulation fitting a marked log-Gaussian Cox model.

+

In addition, the stelfi offers functionality to fit self-exciting spatiotemporal point processes. Models are fitted via maximum likelihood using ‘TMB’ (Template Model Builder) (Kristensen et al. (2016)). Where included 1) random fields are assumed to be Gaussian and are integrated over using the Laplace approximation and 2) a stochastic partial differential equation model, introduced by Lindgren, Rue, and Lindström (2011), is defined for the field(s).

+ + + + +
+ + +
+ + + + \ No newline at end of file diff --git a/docs/lgcp-spatiotemporal.qmd b/docs/lgcp-spatiotemporal.qmd index 48ef1a3..ee1647c 100644 --- a/docs/lgcp-spatiotemporal.qmd +++ b/docs/lgcp-spatiotemporal.qmd @@ -11,6 +11,7 @@ $$G_i(s)=\rho G_{i-1}(s) + \epsilon_i$$ where $\rho$ is a constant between -1 and +1, and $\epsilon_i$ is normally distributed with mean 0. ```{r} +require(stelfi) ndays <- 2 locs <- data.frame(x = xyt$x, y = xyt$y, t = xyt$t) bnd <- INLA::inla.mesh.segment(as.matrix(sf::st_coordinates(domain)[, 1:2])) diff --git a/docs/lgcp.html b/docs/lgcp.html index a9bc921..ef505b0 100644 --- a/docs/lgcp.html +++ b/docs/lgcp.html @@ -275,6 +275,7 @@

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Lindgren, Rue, and Lindström (2011) a Matérn covariance function is used for the covariance of the random field. This takes two parameters \(\tau\) and \(\kappa\), commonly reported as \(r=\frac{\sqrt{8}}{\kappa}\) and \(\sigma=\frac{1}{\sqrt{4\pi\kappa^2\tau^2}}\), where \(r\) is the range and \(\sigma\) is the standard deviation. In the figure above \(\beta = 3\), \(\text{log}(\tau) = 1.6\), and \(\text{log}(\kappa) = 1.95\).

5.1 Delauney triangluations when fitting LGCP models

+

TODO

5.2 Fitting a spatial LGCP

@@ -296,7 +297,7 @@

< fit <- fit_lgcp(locs = locs, sf = us, smesh = smesh, parameters = c(beta = 0, log_tau = log(1), log_kappa = log(1))) -coef(fit, se = TRUE)

+get_coefs(fit)

            Estimate Std. Error
 beta      -0.7659374  0.3558810
diff --git a/docs/lgcp.qmd b/docs/lgcp.qmd
index 28a03de..96d09fb 100644
--- a/docs/lgcp.qmd
+++ b/docs/lgcp.qmd
@@ -9,8 +9,8 @@ Plotted below is a realisation of a LGCP within a disc shaped region overlain on
 ```{r}
 #| echo: false
 #| message: false
-devtools::load_all("~/Git/stelfi")
 require(ggplot2)
+require(stelfi)
 sf <- sf::st_as_sf(spatstat.geom::disc())
 smesh <- fmesher::fm_mesh_2d(loc.domain = sf::st_coordinates(sf)[, 1:2], max.edge = 0.2, cutoff = 0.1)
 parameters <- c(beta = 3, log_tau = 1.6, log_kappa = 1.95)
@@ -56,7 +56,7 @@ smesh <- fmesher::fm_mesh_2d(loc = locs[, 1:2], max.edge = 2, cutoff = 1)
 fit <- fit_lgcp(locs = locs, sf = us, smesh = smesh,
                 parameters = c(beta = 0, log_tau = log(1),
                                log_kappa = log(1)))
-coef(fit, se = TRUE)			       
+get_coefs(fit)			       
 ```
 
 The estimated GMRF can be plotted using the `show_field()` function once the values have been extracted using `get_fields()`.
@@ -101,7 +101,7 @@ The table below gives the estimated parameter values from `stelfi` and `inlabru`
 
 ```{r}
 #| echo: false
-stelfi <- coef(fit, se = TRUE)[c(1, 4, 5),]
+stelfi <- get_coefs(fit)[c(1, 4, 5),]
 params <- data.frame(stelfi = c(paste(round(stelfi[,1], 3), "(", round(stelfi[,2], 3), ")")),
                      inlabru = c(paste(round(pars[,1], 3), "(", round(pars[,2], 3), ")")))
 params <- t(params)
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--git a/docs/marked-hawkes.html b/docs/marked-hawkes.html index 2655d41..ce2852f 100644 --- a/docs/marked-hawkes.html +++ b/docs/marked-hawkes.html @@ -276,7 +276,7 @@

Below data are simulated using the emhawkes package (Lee 2023) where the marks (a vector that scale the jump sizes, starting at 0) are integer values \(\in [1,4]\) for \(t > 0\) and \(0\) for \(t = 0\) (see Section 3.2 for how to simulate using stelfi). The parameter values of the conditional intensity are \(\mu = 1.3\), \(\alpha = 0.4\), and \(\beta = 1.5\). The jump sizes for the possible mark values are shown below.

-
ℹ Loading stelfi
+
Loading required package: stelfi
@@ -390,41 +390,32 @@

3.3 The negative log-likelihood

Below the negative log-likelihood of a univariate marked Hawkes process used by stelfi is written using R syntax and then RTMB (Kristensen 2024) is used for parameter estimation. See here for and overview of RTMB. This section is for demonstration only, feel free to modify the function as desired. Note that this can be used to fit an unmarked model by setting the vector of marks to be \(\boldsymbol{1}\).

-
library(RTMB)
-
-

-Attaching package: 'RTMB'
-
-
-
The following objects are masked from 'package:stelfi':
-
-    MakeADFun, sdreport
-
-
univariate_marked_hawkes <- function(params){
-    getAll(data, params)
-    mu <- exp(log_mu)
-    beta <- exp(log_beta)
-    alpha <- exp(logit_abratio) / (1 + exp(logit_abratio)) * (beta/mean(marks))
-    n <- length(times)
-    last <- times[n]
-    nll <- 0
-    A <- advector(numeric(n)) 
-    for(i in 2:n){
-        A[i] <- sum(exp(-beta * (times[i] - times[i - 1])) * (marks[i - 1] + A[i - 1]))
-    }
-    term_3vec <- log(mu + alpha * A)
-    nll <- (mu * last) + ((alpha/beta) * (sum(marks) - marks[n] - A[n])) - sum(term_3vec)
-    ADREPORT(mu)
-    ADREPORT(alpha)
-    ADREPORT(beta)
-    return(nll)
-}
-
-data <- list(times = res$arrival, marks = res$mark)
-params <- list(log_mu = log(1.3), logit_abratio = 0.6, log_beta = log(1.5))
-obj <- MakeADFun(univariate_marked_hawkes, params, silent = TRUE)
-opt <- nlminb(obj$par, obj$fn, obj$gr)
-summary(sdreport(obj), "report")
+
library(RTMB)
+univariate_marked_hawkes <- function(params){
+    getAll(data, params)
+    mu <- exp(log_mu)
+    beta <- exp(log_beta)
+    alpha <- exp(logit_abratio) / (1 + exp(logit_abratio)) * (beta/mean(marks))
+    n <- length(times)
+    last <- times[n]
+    nll <- 0
+    A <- advector(numeric(n)) 
+    for(i in 2:n){
+        A[i] <- sum(exp(-beta * (times[i] - times[i - 1])) * (marks[i - 1] + A[i - 1]))
+    }
+    term_3vec <- log(mu + alpha * A)
+    nll <- (mu * last) + ((alpha/beta) * (sum(marks) - marks[n] - A[n])) - sum(term_3vec)
+    ADREPORT(mu)
+    ADREPORT(alpha)
+    ADREPORT(beta)
+    return(nll)
+}
+
+data <- list(times = res$arrival, marks = res$mark)
+params <- list(log_mu = log(1.3), logit_abratio = 0.6, log_beta = log(1.5))
+obj <- MakeADFun(univariate_marked_hawkes, params, silent = TRUE)
+opt <- nlminb(obj$par, obj$fn, obj$gr)
+summary(sdreport(obj), "report")
       Estimate Std. Error
 mu    1.1742764 0.25196080
diff --git a/docs/marked-hawkes.qmd b/docs/marked-hawkes.qmd
index 9492e35..1b2d602 100644
--- a/docs/marked-hawkes.qmd
+++ b/docs/marked-hawkes.qmd
@@ -11,9 +11,9 @@ Below data are simulated using the `emhawkes` package [@emhawkes] where the mark
 
 ```{r}
 #| echo: false
-devtools::load_all("~/Git/stelfi")
- kableExtra::kable_styling(
- knitr::kable( data.frame(mark = c(1,2,3,4), jump = c(0.4, 0.8, 1.2, 1.6))), full_width = FALSE)
+require(stelfi)
+kableExtra::kable_styling(
+knitr::kable( data.frame(mark = c(1,2,3,4), jump = c(0.4, 0.8, 1.2, 1.6))), full_width = FALSE)
 ```
 
 ```{r}
diff --git a/docs/marked-hawkes_files/figure-html/unnamed-chunk-5-1.png b/docs/marked-hawkes_files/figure-html/unnamed-chunk-5-1.png
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b/docs/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,171 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; +} + +code span.at { + color: #657422; +} + +code span.ss { + color: #20794D; +} + +code span.an { + color: #5E5E5E; +} + +code span.fu { + color: #4758AB; +} + +code span.st { + color: #20794D; +} + +code span.cf { + color: #003B4F; +} + +code span.op { + color: #5E5E5E; +} + +code span.er { + color: #AD0000; +} + +code span.bn { + color: #AD0000; +} + +code span.al { + color: #AD0000; +} + +code span.va { + color: #111111; +} + +code span.pp { + color: #AD0000; +} + +code span.in { + color: #5E5E5E; +} + +code span.vs { + color: #20794D; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; +} + +code span.ch { + color: #20794D; +} + +code span.dt { + color: #AD0000; +} + +code span.fl { + color: #AD0000; +} + +code span.co { + color: #5E5E5E; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; +} + +code span.sc { + color: #5E5E5E; +} + +code span.dv { + color: #AD0000; +} + +code span.kw { + color: #003B4F; +} + +.prevent-inlining { + content: " { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append(titleEl.innerText, toggleIcon); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + const elRect = el.getBoundingClientRect(); + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + positionToggle(); + }, 50) + ); + positionToggle(); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + nexttick(() => { + let lastBottom = 0; + for (const marginChild of marginChildren) { + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + }); + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const top = + el.getBoundingClientRect().top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightSideConflictEls)); + sidebarScrollVisiblity(toRegions(leftSideConflictEls)); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility(toRegions(leftSideConflictEls)); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/docs/site_libs/quarto-html/tippy.css 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/dev/null +++ b/docs/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,222 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + 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(header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver( + 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0===i?I.isCaseSensitive:i,c=n.includeMatches,a=void 0===c?I.includeMatches:c,s=n.minMatchCharLength,u=void 0===s?I.minMatchCharLength:s,h=n.ignoreLocation,f=void 0===h?I.ignoreLocation:h,l=n.findAllMatches,d=void 0===l?I.findAllMatches:l,v=n.location,g=void 0===v?I.location:v,y=n.threshold,p=void 0===y?I.threshold:y,m=n.distance,k=void 0===m?I.distance:m;r(this,e),this.query=null,this.options={isCaseSensitive:o,includeMatches:a,minMatchCharLength:u,findAllMatches:d,ignoreLocation:f,location:g,threshold:p,distance:k},this.pattern=o?t:t.toLowerCase(),this.query=Z(this.pattern,this.options)}return o(e,[{key:"searchIn",value:function(e){var t=this.query;if(!t)return{isMatch:!1,score:1};var n=this.options,r=n.includeMatches;e=n.isCaseSensitive?e:e.toLowerCase();for(var i=0,o=[],c=0,a=0,s=t.length;a-1&&(n.refIndex=e.idx),t.matches.push(n)}}))}function ve(e,t){t.score=e.score}function ge(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=n.includeMatches,i=void 0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||R(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n1&&void 0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return le(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(fe(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=fe(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..6fd4b5b --- /dev/null +++ b/docs/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1123 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query !== null && mainEl) { + // perform any highlighting + highlight(query, mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query !== null && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" + ? "all" + : quartoSearchOptions.location === "navbar" + ? "(max-width: 991px)" + : "none"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + plugins, + classNames: { + form: "d-flex", + }, + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + return createElement( + "div", + { class: "quarto-search-no-results" }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh + ); + }, + }, + }, + ]; + }, + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = () => { + setIsOpen(false); + }; + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.map((event) => { + const maxEvents = event.objectIDs.slice(0, 20); + return { + ...event, + objectIDs: maxEvents, + }; + }); + + insights.viewedObjectIDs(...events); + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + // create fuse index + const options = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, + }; + const fuse = new window.Fuse([], options); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard(createElement, icon, title, section, text, href) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + [iconEl, titleEl] + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text"].forEach((keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + }); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +function fuseSearch(query, fuse, fuseOptions) { + return fuse.search(query, fuseOptions).map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? 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zHsm~%^%qDUo{Kp%%kO-V=fE1e9Zi}zF^h0W4m=PMlj9Z=u3ghlyUr|WXrV%i z1$<{OIOP)wak_T{R*6;I3~`ha1?aNrcX?O66Fp+ee;rgRB{Ev$>whmJ^S^=Q`44~c bR6~oordR8BgzGg*%cdF_>&H%X`0M`wCY&%4 literal 0 HcmV?d00001 diff --git a/docs/search.json b/docs/search.json index 8a3e8d1..a1fc5f7 100644 --- a/docs/search.json +++ b/docs/search.json @@ -11,7 +11,7 @@ "href": "index.html#installation", "title": "The R package stelfi", "section": "Installation", - "text": "Installation\n\n\n\nOption 1: stelfi is on CRAN\n\ninstall.packages(\"stelfi\")\n\nOption 2: the development version of stelfi package can be installed from GitHub\n\ndevtools::install_github(\"cmjt/stelfi\")\n\n\n\n\n\n\n\nNote\n\n\n\nTo cite stelfi use\n\ncitation(\"stelfi\")\n\nTo cite package 'stelfi' in publications use:\n\n Jones-Todd C, van Helsdingen A (2023). _stelfi: Hawkes and\n Log-Gaussian Cox Point Processes Using Template Model Builder_. R\n package version 1.0.1, .\n\n Jones-Todd CM, van Helsdingen ABM (2024). \"stelfi: An R package for\n fitting Hawkes and log-Gaussian Cox point process models.\" _Ecology\n and Evolution_, *14*, e11005. doi:10.1002/ece3.11005\n .\n\nTo see these entries in BibTeX format, use 'print(,\nbibtex=TRUE)', 'toBibtex(.)', or set\n'options(citation.bibtex.max=999)'." + "text": "Installation\n\n\nℹ Loading stelfi\n\n\nOption 1: stelfi is on CRAN\n\ninstall.packages(\"stelfi\")\n\nOption 2: the development version of stelfi package can be installed from GitHub\n\ndevtools::install_github(\"cmjt/stelfi\")\n\n\n\n\n\n\n\nNote\n\n\n\nTo cite stelfi use\n\ncitation(\"stelfi\")\n\nTo cite `stelfi` in pubications use:\n\n C. M. Jones-Todd, and A. B. M. van Helsdingen ABM (2024), stelfi: An\n R package for fitting Hawkes and log-Gaussian Cox point process\n models, Ecology and Evolution, 14, e11005,doi:10.1002/ece3.11005\n\nA BibTeX entry for LaTeX users is\n\n @Article{,\n title = {stelfi: An R package for fitting Hawkes and log-Gaussian Cox point process models},\n author = {Charlotte M. Jones-Todd and Alec B. M. {van Helsdingen}},\n year = {2024},\n journal = {Ecology and Evolution},\n volume = {14},\n pages = {e11005},\n doi = {10.1002/ece3.11005},\n }" }, { "objectID": "intro.html", @@ -25,14 +25,14 @@ "href": "univariate-hawkes.html#sec-simulate-hawkes", "title": "2  Univariate Hawkes", "section": "2.1 Simulating from a univariate Hawkes model", - "text": "2.1 Simulating from a univariate Hawkes model\nThe function sim_hawkes() offers two simulation methods for simulating a realisation of a univariate Hawkes process. The default, with method = 1, uses the same method as algorithm 2 in Ogata (1981), simulating until a given time horizon (set by argument n).\n\nsim <- sim_hawkes(mu = 0.5, alpha = 1, beta = 1.5, n = 40, plot = TRUE)\n\n\n\n\nThe second option, setting method = 2, uses an accept/reject framework and the argument n specifies the number of points to simulate (default n = 100).\n\nsim <- sim_hawkes(mu = 0.5, alpha = 1, beta = 1.5, plot = TRUE, method = 2)" + "text": "2.1 Simulating from a univariate Hawkes model\nThe function sim_hawkes() offers two simulation methods for simulating a realisation of a univariate Hawkes process. The default, with method = 1, uses the same method as algorithm 2 in Ogata (1981), simulating until a given time horizon (set by argument n).\n\nsim <- sim_hawkes(mu = 0.5, alpha = 1, beta = 1.5, n = 40, plot = TRUE)\n\nThe second option, setting method = 2, uses an accept/reject framework and the argument n specifies the number of points to simulate (default n = 100).\n\nsim <- sim_hawkes(mu = 0.5, alpha = 1, beta = 1.5, plot = TRUE, method = 2)" }, { "objectID": "univariate-hawkes.html#fitting-a-univariate-hawkes-model", "href": "univariate-hawkes.html#fitting-a-univariate-hawkes-model", "title": "2  Univariate Hawkes", "section": "2.2 Fitting a univariate Hawkes model", - "text": "2.2 Fitting a univariate Hawkes model\n\ndevtools::load_all(\"~/Git/stelfi\")\n\nℹ Loading stelfi\n\n## require(stelfi)\n\nTo fit a univariate Hawkes model in stelfi use the function fit_hawkes() with the following required arguments\n\ntimes - a vector of numeric occurrence times, and\nparameters - a vector of named starting values for \\(\\mu\\) (mu), \\(\\alpha\\) (alpha), and \\(\\beta\\) (beta).\n\nThe function get_coefs() can then be called on the fitted model object to return the estimated parameter values.\n\n2.2.1 A simulated example\nSimulating a realisation of a univariate Hawkes process (see Section 2.1 for more details) with \\(\\mu = 1.3\\), \\(\\alpha = 0.4\\), and \\(\\beta = 1.5\\) (over \\(t \\in [0, 500]\\)).\n\ntimes <- sim_hawkes(mu = 1.3, alpha = 0.4, beta = 1.5, n = 500)\n\n\n## starting values\nsv <- c(mu = 1.3, alpha = 0.4, beta = 1.5)\n## using stelfi\nfit <- fit_hawkes(times = times, parameters = sv)\nstelfi <- get_coefs(fit)\nstelfi\n\n Estimate Std. Error\nmu 1.3997320 0.1125668\nalpha 0.3737118 0.1207772\nbeta 1.7730216 0.7495558\n\n\nAs a comparison, below emhawkes (Lee (2023)), and hawkesbow (Cheysson (2021)) are used to fit a univariate Hawkes process to the same simulated data.\n\n## benchmark using emhawkes\nrequire(emhawkes)\nh <- new(\"hspec\", mu = sv[1], alpha = sv[2], beta = sv[3])\n## emhawkes requires the inter arrival times to fit the model\ninter <- diff(times)\nfit_em <- hfit(object = h, inter_arrival = inter)\nem <- summary(fit_em)$estimate\nem\n\n Estimate Std. error t value Pr(> t)\nmu1 1.3976553 0.1091437 12.805644 1.524557e-37\nalpha1 0.3683585 0.1380857 2.667608 7.639335e-03\nbeta1 1.7380101 0.8236957 2.110015 3.485708e-02\n\n## bench mark using hawkesbow\nrequire(hawkesbow)\nfit_bow <- mle(events = times, kern = \"Exponential\", end = max(times))\n## use the Hessian to obtain the standard errors from hawkesbow\nbow <- cbind(Estimate = fit_bow$par,\n \"Std. error\" = -fit_bow$model$ddloglik(times, max(times)) |> solve() |> diag() |> sqrt())\nbow\n\n Estimate Std. error\n[1,] 1.3998368 0.11238176\n[2,] 0.2107781 0.05814911\n[3,] 1.7724499 0.74877227\n\n\nThe table below gives the estimated parameter values from each of stelfi, emhawkes, and hawkesbow along with the standard errors in brackets. Note that hawkesbow estimates \\(\\frac{\\alpha}{\\beta}\\) rather that \\(\\beta\\) directly, and that the standard errors are computed here using the returned Hessian matrix \\(H\\) (i.e., \\(\\sqrt{\\text{diag}{-(H^{-1})}}\\)).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\(\\mu\\)\n\\(\\alpha\\)\n\\(\\beta\\)\n\\(\\frac{\\alpha}{\\beta}\\)\n\n\n\n\nTRUTH\n1.300\n0.400\n1.500\n0.267\n\n\nstelfi\n1.4 ( 0.113 )\n0.374 ( 0.121 )\n1.773 ( 0.75 )\n-\n\n\nemhawkes\n1.398 ( 0.109 )\n0.368 ( 0.138 )\n1.738 ( 0.824 )\n-\n\n\nhawkesbow\n1.4 ( 0.112 )\n-\n1.772 ( 0.749 )\n0.211 ( 0.058 )\n\n\n\n\n\n\n\n2.2.2 An applied example\nA NIWA scientist found a working USB in the scat of a leopard seal, they then tweeted about it in the hopes of finding its owner1. The dates and times of these tweets and retweets are available in stelfi as retweets_niwa.\n\ndata(retweets_niwa)\n\nThe dates/times need to be numeric and sorted in ascending order (starting a time \\(t = 0\\)). Note too that there can be no simultaneous events.\n\n## numeric time stamps\ntimes <- unique(sort(as.numeric(difftime(retweets_niwa ,min(retweets_niwa),units = \"mins\"))))\n\nThe histogram below shows the observed counts (4772) of unique retweet times from the original tweet (\\(t=0\\)) to the time (just over two days later) that the owner of the USB came forward, t = 3143 mins.\n\n\n\n\n\nTo fit the model chose some starting values for the parameters and supply these along with the numeric times to the function fit_hawkes().\n\nsv <- c(mu = 9, alpha = 3, beta = 10)\nfit <- fit_hawkes(times = times, parameters = sv) \n\n\n## print out estimated parameters\npars <- get_coefs(fit)\npars\n\n Estimate Std. Error\nmu 0.06328099 0.017783908\nalpha 0.07596531 0.007777899\nbeta 0.07911346 0.008109789\n\n\nFrom the estimated coefficients above\n\nthe expected (estimated) background rate of sightings (i.e., independent sightings) is \\(\\hat{\\mu}T =\\) 0.063 \\(\\times\\) 3143.02 \\(=\\) 198.89, which indicates that ~199 retweets (from the 4772) were principal retweets and that the remaining were due to self-excitement;\nthe expected number of retweets “triggered” by any one retweet is estimated as \\(\\frac{\\hat{\\alpha}}{\\hat{\\beta}}\\) = 0.96 (note the maximum this can possibly be is 1);\nthe expected number of descendants per retweet is estimated as \\(\\frac{\\hat{\\beta}}{\\hat{\\beta} - \\hat{\\alpha}}\\) = 25.13;\nthe rate of decay for the self-excitement is estimated as \\(\\frac{1}{\\hat{\\beta}}\\) = 12.64, indicating that after ~13 minutes a retweet is likely unrelated2 to the previous retweet.\n\nThe show_hawkes() function can be called on the fitted model object to plot the estimated conditional intensity and the data, top and bottom panels below respectively.\n\nshow_hawkes(fit)" + "text": "2.2 Fitting a univariate Hawkes model\n\nrequire(stelfi)\n\nTo fit a univariate Hawkes model in stelfi use the function fit_hawkes() with the following required arguments\n\ntimes - a vector of numeric occurrence times, and\nparameters - a vector of named starting values for \\(\\mu\\) (mu), \\(\\alpha\\) (alpha), and \\(\\beta\\) (beta).\n\nThe function get_coefs() can then be called on the fitted model object to return the estimated parameter values.\n\n2.2.1 A simulated example\nSimulating a realisation of a univariate Hawkes process (see Section 2.1 for more details) with \\(\\mu = 1.3\\), \\(\\alpha = 0.4\\), and \\(\\beta = 1.5\\) (over \\(t \\in [0, 500]\\)).\n\ntimes <- sim_hawkes(mu = 1.3, alpha = 0.4, beta = 1.5, n = 500)\n\n\n## starting values\nsv <- c(mu = 1.3, alpha = 0.4, beta = 1.5)\n## using stelfi\nfit <- fit_hawkes(times = times, parameters = sv)\nstelfi <- get_coefs(fit)\nstelfi\n\n Estimate Std. Error\nmu 1.3997320 0.1125668\nalpha 0.3737118 0.1207772\nbeta 1.7730216 0.7495558\n\n\nAs a comparison, below emhawkes (Lee (2023)), and hawkesbow (Cheysson (2021)) are used to fit a univariate Hawkes process to the same simulated data.\n\n## benchmark using emhawkes\nrequire(emhawkes)\nh <- new(\"hspec\", mu = sv[1], alpha = sv[2], beta = sv[3])\n## emhawkes requires the inter arrival times to fit the model\ninter <- diff(times)\nfit_em <- hfit(object = h, inter_arrival = inter)\nem <- summary(fit_em)$estimate\nem\n\n Estimate Std. error t value Pr(> t)\nmu1 1.3976553 0.1091437 12.805644 1.524557e-37\nalpha1 0.3683585 0.1380857 2.667608 7.639335e-03\nbeta1 1.7380101 0.8236957 2.110015 3.485708e-02\n\n## bench mark using hawkesbow\nrequire(hawkesbow)\nfit_bow <- mle(events = times, kern = \"Exponential\", end = max(times))\n## use the Hessian to obtain the standard errors from hawkesbow\nbow <- cbind(Estimate = fit_bow$par,\n \"Std. error\" = -fit_bow$model$ddloglik(times, max(times)) |> solve() |> diag() |> sqrt())\nbow\n\n Estimate Std. error\n[1,] 1.3998368 0.11238176\n[2,] 0.2107781 0.05814911\n[3,] 1.7724499 0.74877227\n\n\nThe table below gives the estimated parameter values from each of stelfi, emhawkes, and hawkesbow along with the standard errors in brackets. Note that hawkesbow estimates \\(\\frac{\\alpha}{\\beta}\\) rather that \\(\\beta\\) directly, and that the standard errors are computed here using the returned Hessian matrix \\(H\\) (i.e., \\(\\sqrt{\\text{diag}{-(H^{-1})}}\\)).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\(\\mu\\)\n\\(\\alpha\\)\n\\(\\beta\\)\n\\(\\frac{\\alpha}{\\beta}\\)\n\n\n\n\nTRUTH\n1.300\n0.400\n1.500\n0.267\n\n\nstelfi\n1.4 ( 0.113 )\n0.374 ( 0.121 )\n1.773 ( 0.75 )\n-\n\n\nemhawkes\n1.398 ( 0.109 )\n0.368 ( 0.138 )\n1.738 ( 0.824 )\n-\n\n\nhawkesbow\n1.4 ( 0.112 )\n-\n1.772 ( 0.749 )\n0.211 ( 0.058 )\n\n\n\n\n\n\n\n2.2.2 An applied example\nA NIWA scientist found a working USB in the scat of a leopard seal, they then tweeted about it in the hopes of finding its owner1. The dates and times of these tweets and retweets are available in stelfi as retweets_niwa.\n\ndata(retweets_niwa)\n\nThe dates/times need to be numeric and sorted in ascending order (starting a time \\(t = 0\\)). Note too that there can be no simultaneous events.\n\n## numeric time stamps\ntimes <- unique(sort(as.numeric(difftime(retweets_niwa ,min(retweets_niwa),units = \"mins\"))))\n\nThe histogram below shows the observed counts (4772) of unique retweet times from the original tweet (\\(t=0\\)) to the time (just over two days later) that the owner of the USB came forward, t = 3143 mins.\n\n\n\n\n\nTo fit the model chose some starting values for the parameters and supply these along with the numeric times to the function fit_hawkes().\n\nsv <- c(mu = 9, alpha = 3, beta = 10)\nfit <- fit_hawkes(times = times, parameters = sv) \n\n\n## print out estimated parameters\npars <- get_coefs(fit)\npars\n\n Estimate Std. Error\nmu 0.06328099 0.017783908\nalpha 0.07596531 0.007777899\nbeta 0.07911346 0.008109789\n\n\nFrom the estimated coefficients above\n\nthe expected (estimated) background rate of sightings (i.e., independent sightings) is \\(\\hat{\\mu}T =\\) 0.063 \\(\\times\\) 3143.02 \\(=\\) 198.89, which indicates that ~199 retweets (from the 4772) were principal retweets and that the remaining were due to self-excitement;\nthe expected number of retweets “triggered” by any one retweet is estimated as \\(\\frac{\\hat{\\alpha}}{\\hat{\\beta}}\\) = 0.96 (note the maximum this can possibly be is 1);\nthe expected number of descendants per retweet is estimated as \\(\\frac{\\hat{\\beta}}{\\hat{\\beta} - \\hat{\\alpha}}\\) = 25.13;\nthe rate of decay for the self-excitement is estimated as \\(\\frac{1}{\\hat{\\beta}}\\) = 12.64, indicating that after ~13 minutes a retweet is likely unrelated2 to the previous retweet.\n\nThe show_hawkes() function can be called on the fitted model object to plot the estimated conditional intensity and the data, top and bottom panels below respectively.\n\nshow_hawkes(fit)" }, { "objectID": "univariate-hawkes.html#sec-gof-hawkes", @@ -53,7 +53,7 @@ "href": "marked-hawkes.html#a-simulated-example", "title": "3  Marked Hawkes", "section": "3.1 A simulated example", - "text": "3.1 A simulated example\nBelow data are simulated using the emhawkes package (Lee 2023) where the marks (a vector that scale the jump sizes, starting at 0) are integer values \\(\\in [1,4]\\) for \\(t > 0\\) and \\(0\\) for \\(t = 0\\) (see Section 3.2 for how to simulate using stelfi). The parameter values of the conditional intensity are \\(\\mu = 1.3\\), \\(\\alpha = 0.4\\), and \\(\\beta = 1.5\\). The jump sizes for the possible mark values are shown below.\n\n\nℹ Loading stelfi\n\n\n\n\n \n \n mark \n jump \n \n \n\n \n 1 \n 0.4 \n \n \n 2 \n 0.8 \n \n \n 3 \n 1.2 \n \n \n 4 \n 1.6 \n \n\n\n\n\n\n\nrequire(emhawkes)\nmu <- 1.3; alpha <- 0.4; beta <- 1.5\nfn_mark <- function( ...){\n sample(1:4, 1)\n}\nh1 <- new(\"hspec\", mu = mu, alpha = alpha, beta = beta,\n rmark = fn_mark)\nset.seed(123)\nres <- hsim(h1, size = 100)\n\nTo fit the model in stelfi the fit_hawkes() function is used and the additional optional argument marks supplied.\n\nsv <- c(mu = 1.3, alpha = 0.4, beta = 1.5)\nfit <- fit_hawkes(times = res$arrival, parameters = sv, marks = res$mark)\nget_coefs(fit)\n\n Estimate Std. Error\nmu 1.1741551 0.25195698\nalpha 0.1008830 0.06935281\nbeta 0.8849185 0.50179668\n\n\nNote the estimated coefficient \\(\\alpha\\) from stelfi equates to \\(\\frac{\\alpha}{\\text{mark}}\\) from emhawkes.\n\n## benchmark emhawkes\nemhawkes::hfit(h1, inter_arrival = res$inter_arrival, mark = res$mark) |>\n summary()\n\n--------------------------------------------\nMaximum Likelihood estimation\nBFGS maximization, 43 iterations\nReturn code 0: successful convergence \nLog-Likelihood: -51.99802 \n3 free parameters\nEstimates:\n Estimate Std. error t value Pr(> t) \nmu1 1.1106 0.2888 3.845 0.00012 ***\nalpha1 0.2787 0.1903 1.464 0.14309 \nbeta1 0.9277 0.5693 1.629 0.10321 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n--------------------------------------------\n\n\nThe fitted model and diagnostic plots are plotted using show_hawkes() and show_hawkes_GOF().\n\nshow_hawkes(fit)\n\n\n\n\n\nshow_hawkes_GOF(fit)\n\n\n Exact one-sample Kolmogorov-Smirnov test\n\ndata: interarrivals\nD = 0.09164, p-value = 0.355\nalternative hypothesis: two-sided\n\n\n Box-Ljung test\n\ndata: interarrivals\nX-squared = 0.38285, df = 1, p-value = 0.5361" + "text": "3.1 A simulated example\nBelow data are simulated using the emhawkes package (Lee 2023) where the marks (a vector that scale the jump sizes, starting at 0) are integer values \\(\\in [1,4]\\) for \\(t > 0\\) and \\(0\\) for \\(t = 0\\) (see Section 3.2 for how to simulate using stelfi). The parameter values of the conditional intensity are \\(\\mu = 1.3\\), \\(\\alpha = 0.4\\), and \\(\\beta = 1.5\\). The jump sizes for the possible mark values are shown below.\n\n\nLoading required package: stelfi\n\n\n\n\n \n \n mark \n jump \n \n \n\n \n 1 \n 0.4 \n \n \n 2 \n 0.8 \n \n \n 3 \n 1.2 \n \n \n 4 \n 1.6 \n \n\n\n\n\n\n\nrequire(emhawkes)\nmu <- 1.3; alpha <- 0.4; beta <- 1.5\nfn_mark <- function( ...){\n sample(1:4, 1)\n}\nh1 <- new(\"hspec\", mu = mu, alpha = alpha, beta = beta,\n rmark = fn_mark)\nset.seed(123)\nres <- hsim(h1, size = 100)\n\nTo fit the model in stelfi the fit_hawkes() function is used and the additional optional argument marks supplied.\n\nsv <- c(mu = 1.3, alpha = 0.4, beta = 1.5)\nfit <- fit_hawkes(times = res$arrival, parameters = sv, marks = res$mark)\nget_coefs(fit)\n\n Estimate Std. Error\nmu 1.1741551 0.25195698\nalpha 0.1008830 0.06935281\nbeta 0.8849185 0.50179668\n\n\nNote the estimated coefficient \\(\\alpha\\) from stelfi equates to \\(\\frac{\\alpha}{\\text{mark}}\\) from emhawkes.\n\n## benchmark emhawkes\nemhawkes::hfit(h1, inter_arrival = res$inter_arrival, mark = res$mark) |>\n summary()\n\n--------------------------------------------\nMaximum Likelihood estimation\nBFGS maximization, 43 iterations\nReturn code 0: successful convergence \nLog-Likelihood: -51.99802 \n3 free parameters\nEstimates:\n Estimate Std. error t value Pr(> t) \nmu1 1.1106 0.2888 3.845 0.00012 ***\nalpha1 0.2787 0.1903 1.464 0.14309 \nbeta1 0.9277 0.5693 1.629 0.10321 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n--------------------------------------------\n\n\nThe fitted model and diagnostic plots are plotted using show_hawkes() and show_hawkes_GOF().\n\nshow_hawkes(fit)\n\n\n\n\n\nshow_hawkes_GOF(fit)\n\n\n Exact one-sample Kolmogorov-Smirnov test\n\ndata: interarrivals\nD = 0.09164, p-value = 0.355\nalternative hypothesis: two-sided\n\n\n Box-Ljung test\n\ndata: interarrivals\nX-squared = 0.38285, df = 1, p-value = 0.5361" }, { "objectID": "marked-hawkes.html#sec-simulate-marked-hawkes", @@ -67,7 +67,7 @@ "href": "marked-hawkes.html#the-negative-log-likelihood", "title": "3  Marked Hawkes", "section": "3.3 The negative log-likelihood", - "text": "3.3 The negative log-likelihood\nBelow the negative log-likelihood of a univariate marked Hawkes process used by stelfi is written using R syntax and then RTMB (Kristensen 2024) is used for parameter estimation. See here for and overview of RTMB. This section is for demonstration only, feel free to modify the function as desired. Note that this can be used to fit an unmarked model by setting the vector of marks to be \\(\\boldsymbol{1}\\).\n\nlibrary(RTMB)\n\n\nAttaching package: 'RTMB'\n\n\nThe following objects are masked from 'package:stelfi':\n\n MakeADFun, sdreport\n\nunivariate_marked_hawkes <- function(params){\n getAll(data, params)\n mu <- exp(log_mu)\n beta <- exp(log_beta)\n alpha <- exp(logit_abratio) / (1 + exp(logit_abratio)) * (beta/mean(marks))\n n <- length(times)\n last <- times[n]\n nll <- 0\n A <- advector(numeric(n)) \n for(i in 2:n){\n A[i] <- sum(exp(-beta * (times[i] - times[i - 1])) * (marks[i - 1] + A[i - 1]))\n }\n term_3vec <- log(mu + alpha * A)\n nll <- (mu * last) + ((alpha/beta) * (sum(marks) - marks[n] - A[n])) - sum(term_3vec)\n ADREPORT(mu)\n ADREPORT(alpha)\n ADREPORT(beta)\n return(nll)\n}\n\ndata <- list(times = res$arrival, marks = res$mark)\nparams <- list(log_mu = log(1.3), logit_abratio = 0.6, log_beta = log(1.5))\nobj <- MakeADFun(univariate_marked_hawkes, params, silent = TRUE)\nopt <- nlminb(obj$par, obj$fn, obj$gr)\nsummary(sdreport(obj), \"report\")\n\n Estimate Std. Error\nmu 1.1742764 0.25196080\nalpha 0.1008268 0.06934413\nbeta 0.8846088 0.50173890\n\n\n\n\n\n\nKristensen, Kasper. 2024. RTMB: ’R’ Bindings for ’TMB’. https://CRAN.R-project.org/package=RTMB.\n\n\nLee, Kyungsub. 2023. Emhawkes: Exponential Multivariate Hawkes Model. https://CRAN.R-project.org/package=emhawkes." + "text": "3.3 The negative log-likelihood\nBelow the negative log-likelihood of a univariate marked Hawkes process used by stelfi is written using R syntax and then RTMB (Kristensen 2024) is used for parameter estimation. See here for and overview of RTMB. This section is for demonstration only, feel free to modify the function as desired. Note that this can be used to fit an unmarked model by setting the vector of marks to be \\(\\boldsymbol{1}\\).\n\nlibrary(RTMB)\nunivariate_marked_hawkes <- function(params){\n getAll(data, params)\n mu <- exp(log_mu)\n beta <- exp(log_beta)\n alpha <- exp(logit_abratio) / (1 + exp(logit_abratio)) * (beta/mean(marks))\n n <- length(times)\n last <- times[n]\n nll <- 0\n A <- advector(numeric(n)) \n for(i in 2:n){\n A[i] <- sum(exp(-beta * (times[i] - times[i - 1])) * (marks[i - 1] + A[i - 1]))\n }\n term_3vec <- log(mu + alpha * A)\n nll <- (mu * last) + ((alpha/beta) * (sum(marks) - marks[n] - A[n])) - sum(term_3vec)\n ADREPORT(mu)\n ADREPORT(alpha)\n ADREPORT(beta)\n return(nll)\n}\n\ndata <- list(times = res$arrival, marks = res$mark)\nparams <- list(log_mu = log(1.3), logit_abratio = 0.6, log_beta = log(1.5))\nobj <- MakeADFun(univariate_marked_hawkes, params, silent = TRUE)\nopt <- nlminb(obj$par, obj$fn, obj$gr)\nsummary(sdreport(obj), \"report\")\n\n Estimate Std. Error\nmu 1.1742764 0.25196080\nalpha 0.1008268 0.06934413\nbeta 0.8846088 0.50173890\n\n\n\n\n\n\nKristensen, Kasper. 2024. RTMB: ’R’ Bindings for ’TMB’. https://CRAN.R-project.org/package=RTMB.\n\n\nLee, Kyungsub. 2023. Emhawkes: Exponential Multivariate Hawkes Model. https://CRAN.R-project.org/package=emhawkes." }, { "objectID": "multi-hawkes.html#a-simulated-example", @@ -81,14 +81,14 @@ "href": "lgcp.html#sec-mesh", "title": "5  Log-Gaussian Cox process", "section": "5.1 Delauney triangluations when fitting LGCP models", - "text": "5.1 Delauney triangluations when fitting LGCP models" + "text": "5.1 Delauney triangluations when fitting LGCP models\nTODO" }, { "objectID": "lgcp.html#fitting-a-spatial-lgcp", "href": "lgcp.html#fitting-a-spatial-lgcp", "title": "5  Log-Gaussian Cox process", "section": "5.2 Fitting a spatial LGCP", - "text": "5.2 Fitting a spatial LGCP\nUsing the applied example given in Jones-Todd and van Helsdingen (2024) a LGCP model is fitted to sasquatch sightings using the function fit_lgcp(). For more detais on the use of the Delauney triangluation see Section 5.1.\n\ndata(\"sasquatch\", package = \"stelfi\")\n## get sf of the contiguous US\nsf::sf_use_s2(FALSE)\nus <- maps::map(\"usa\", fill = TRUE, plot = FALSE) |>\n sf::st_as_sf() |>\n sf::st_make_valid()\n## dataframe of sighting locations (lat, long)\nlocs <- sf::st_coordinates(sasquatch) |>\n as.data.frame()\nnames(locs) <- c(\"x\", \"y\")\n## Delauney triangluation of domain\nsmesh <- fmesher::fm_mesh_2d(loc = locs[, 1:2], max.edge = 2, cutoff = 1)\n## fit model with user-chosen parameter starting values\nfit <- fit_lgcp(locs = locs, sf = us, smesh = smesh,\n parameters = c(beta = 0, log_tau = log(1),\n log_kappa = log(1)))\ncoef(fit, se = TRUE) \n\n Estimate Std. Error\nbeta -0.7659374 0.3558810\nlog_tau -0.6109840 0.1028264\nlog_kappa -0.9540269 0.1510160\nrange 7.3430015 1.1089108\nstdev 1.3491825 0.1480772\n\n\nThe estimated GMRF can be plotted using the show_field() function once the values have been extracted using get_fields().\n\nget_fields(fit, smesh) |>\n show_field(smesh = smesh, sf = us, clip = TRUE) + ggplot2::theme_classic()\n\n\n\n\nThe estimated intensity surface can be plotted using the show_lambda() function.\n\nshow_lambda(fit, smesh = smesh, sf = us, clip = TRUE) + ggplot2::theme_classic()\n\n\n\n\nAs a comparison, inlabru (Bachl et al. (2019)) is used to fit the same model to these data.\n\nrequire(inlabru)\nrequire(sp)\nlocs_sp <- locs; sp::coordinates(locs_sp) <- c(\"x\", \"y\")\ndomain <- as(us, \"Spatial\")\nmatern <- INLA::inla.spde2.pcmatern(smesh,\nprior.sigma = c(0.1, 0.01),\nprior.range = c(5, 0.01)\n)\n## latent field\ncmp <- coordinates ~ random_field(coordinates, model = matern) + Intercept(1)\nsp::proj4string(locs_sp) <- smesh$crs <- sp::proj4string(domain)\n## fit model\nfit_inla <- lgcp(cmp, locs_sp, samplers = domain, domain = list(coordinates = smesh))\npars <- rbind(fit_inla$summary.fixed[,1:2], fit_inla$summary.hyperpar[,1:2])\npars\n\n mean sd\nIntercept -0.4907638 0.24998054\nRange for random_field 7.9401368 0.81253926\nStdev for random_field 0.8759888 0.06323398\n\n\nThe table below gives the estimated parameter values from stelfi and inlabru, and hawkesbow along with the standard errors in brackets.\n\n\n\n\n\n\n\\(\\beta\\)\n\\(r\\)\n\\(\\sigma\\)\n\n\n\n\nstelfi\n-0.766 ( 0.356 )\n7.343 ( 1.109 )\n1.349 ( 0.148 )\n\n\ninlabru\n-0.491 ( 0.25 )\n7.94 ( 0.813 )\n0.876 ( 0.063 )\n\n\n\n\n\n\n\n\n\nBachl, Fabian E., Finn Lindgren, David L. Borchers, and Janine B. Illian. 2019. “inlabru: An R Package for Bayesian Spatial Modelling from Ecological Survey Data.” Methods in Ecology and Evolution 10: 760–66. https://doi.org/10.1111/2041-210X.13168.\n\n\nJones-Todd, Charlotte M., and Alec B. M. van Helsdingen. 2024. “Stelfi: An r Package for Fitting Hawkes and Log-Gaussian Cox Point Process Models.” Ecology and Evolution 14: e11005. https://doi.org/10.1002/ece3.11005.\n\n\nLindgren, Finn, Håvard Rue, and Johan Lindström. 2011. “An Explicit Link Between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (with Discussion).” Journal of the Royal Statistical Society B 73 (4): 423–98." + "text": "5.2 Fitting a spatial LGCP\nUsing the applied example given in Jones-Todd and van Helsdingen (2024) a LGCP model is fitted to sasquatch sightings using the function fit_lgcp(). For more detais on the use of the Delauney triangluation see Section 5.1.\n\ndata(\"sasquatch\", package = \"stelfi\")\n## get sf of the contiguous US\nsf::sf_use_s2(FALSE)\nus <- maps::map(\"usa\", fill = TRUE, plot = FALSE) |>\n sf::st_as_sf() |>\n sf::st_make_valid()\n## dataframe of sighting locations (lat, long)\nlocs <- sf::st_coordinates(sasquatch) |>\n as.data.frame()\nnames(locs) <- c(\"x\", \"y\")\n## Delauney triangluation of domain\nsmesh <- fmesher::fm_mesh_2d(loc = locs[, 1:2], max.edge = 2, cutoff = 1)\n## fit model with user-chosen parameter starting values\nfit <- fit_lgcp(locs = locs, sf = us, smesh = smesh,\n parameters = c(beta = 0, log_tau = log(1),\n log_kappa = log(1)))\nget_coefs(fit) \n\n Estimate Std. Error\nbeta -0.7659374 0.3558810\nlog_tau -0.6109840 0.1028264\nlog_kappa -0.9540269 0.1510160\nrange 7.3430015 1.1089108\nstdev 1.3491825 0.1480772\n\n\nThe estimated GMRF can be plotted using the show_field() function once the values have been extracted using get_fields().\n\nget_fields(fit, smesh) |>\n show_field(smesh = smesh, sf = us, clip = TRUE) + ggplot2::theme_classic()\n\n\n\n\nThe estimated intensity surface can be plotted using the show_lambda() function.\n\nshow_lambda(fit, smesh = smesh, sf = us, clip = TRUE) + ggplot2::theme_classic()\n\n\n\n\nAs a comparison, inlabru (Bachl et al. (2019)) is used to fit the same model to these data.\n\nrequire(inlabru)\nrequire(sp)\nlocs_sp <- locs; sp::coordinates(locs_sp) <- c(\"x\", \"y\")\ndomain <- as(us, \"Spatial\")\nmatern <- INLA::inla.spde2.pcmatern(smesh,\nprior.sigma = c(0.1, 0.01),\nprior.range = c(5, 0.01)\n)\n## latent field\ncmp <- coordinates ~ random_field(coordinates, model = matern) + Intercept(1)\nsp::proj4string(locs_sp) <- smesh$crs <- sp::proj4string(domain)\n## fit model\nfit_inla <- lgcp(cmp, locs_sp, samplers = domain, domain = list(coordinates = smesh))\npars <- rbind(fit_inla$summary.fixed[,1:2], fit_inla$summary.hyperpar[,1:2])\npars\n\n mean sd\nIntercept -0.4907638 0.24998054\nRange for random_field 7.9401368 0.81253926\nStdev for random_field 0.8759888 0.06323398\n\n\nThe table below gives the estimated parameter values from stelfi and inlabru, and hawkesbow along with the standard errors in brackets.\n\n\n\n\n\n\n\\(\\beta\\)\n\\(r\\)\n\\(\\sigma\\)\n\n\n\n\nstelfi\n-0.766 ( 0.356 )\n7.343 ( 1.109 )\n1.349 ( 0.148 )\n\n\ninlabru\n-0.491 ( 0.25 )\n7.94 ( 0.813 )\n0.876 ( 0.063 )\n\n\n\n\n\n\n\n\n\nBachl, Fabian E., Finn Lindgren, David L. Borchers, and Janine B. Illian. 2019. “inlabru: An R Package for Bayesian Spatial Modelling from Ecological Survey Data.” Methods in Ecology and Evolution 10: 760–66. https://doi.org/10.1111/2041-210X.13168.\n\n\nJones-Todd, Charlotte M., and Alec B. M. van Helsdingen. 2024. “Stelfi: An r Package for Fitting Hawkes and Log-Gaussian Cox Point Process Models.” Ecology and Evolution 14: e11005. https://doi.org/10.1002/ece3.11005.\n\n\nLindgren, Finn, Håvard Rue, and Johan Lindström. 2011. “An Explicit Link Between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (with Discussion).” Journal of the Royal Statistical Society B 73 (4): 423–98." }, { "objectID": "summary.html", diff --git a/docs/site_libs/bootstrap/bootstrap-icons.css b/docs/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..f51d04b --- /dev/null +++ b/docs/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,1704 @@ +@font-face { + font-family: "bootstrap-icons"; + src: +url("./bootstrap-icons.woff?524846017b983fc8ded9325d94ed40f3") format("woff"); +} + +.bi::before, +[class^="bi-"]::before, +[class*=" bi-"]::before { + display: inline-block; + font-family: bootstrap-icons !important; + 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[page-start-inset] minmax(0px, 145px) [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(450px, calc(750px - 3em)) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start] 35px [page-start-inset] minmax(0px, 145px) [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(450px, calc(750px - 3em)) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(75px, 150px) [page-end-inset] 25px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(25px, 50px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked.fullcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc( 1000px - 3em )) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating.fullcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc(800px - 3em)) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked.slimcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 35px [body-end-outset] minmax(75px, 145px) [page-end-inset] 35px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked.listing .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(25px, 50px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating.slimcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 35px [body-end-outset] minmax(75px, 145px) [page-end-inset] 35px [page-end] 4fr [screen-end-inset] 1.5em [screen-end]}body.floating.listing .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc(750px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(75px, 150px) [page-end-inset] 25px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}}@media(max-width: 767.98px){body .page-columns,body.fullcontent:not(.floating):not(.docked) .page-columns,body.slimcontent:not(.floating):not(.docked) .page-columns,body.docked .page-columns,body.docked.slimcontent 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body-content-start] minmax(0px, 1fr) [body-content-end body-end body-end-outset page-end-inset page-end screen-end-inset] 1.5em [screen-end]}nav[role=doc-toc]{display:none}}body,.page-row-navigation{grid-template-rows:[page-top] max-content [contents-top] max-content [contents-bottom] max-content [page-bottom]}.page-rows-contents{grid-template-rows:[content-top] minmax(max-content, 1fr) [content-bottom] minmax(60px, max-content) [page-bottom]}.page-full{grid-column:screen-start/screen-end !important}.page-columns>*{grid-column:body-content-start/body-content-end}.page-columns.column-page>*{grid-column:page-start/page-end}.page-columns.column-page-left>*{grid-column:page-start/body-content-end}.page-columns.column-page-right>*{grid-column:body-content-start/page-end}.page-rows{grid-auto-rows:auto}.header{grid-column:screen-start/screen-end;grid-row:page-top/contents-top}#quarto-content{padding:0;grid-column:screen-start/screen-end;grid-row:contents-top/contents-bottom}body.floating 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.column-page-inset-right{grid-column:body-content-start/page-end-inset;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-page-inset-right figcaption table{background:#fff}.page-columns .column-page-left{grid-column:page-start/body-content-end;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-page-left table{background:#fff}.page-columns .column-page-right{grid-column:body-content-start/page-end;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-page-right figcaption table{background:#fff}#quarto-content.page-columns #quarto-margin-sidebar,#quarto-content.page-columns #quarto-sidebar{z-index:1}@media(max-width: 991.98px){#quarto-content.page-columns #quarto-margin-sidebar.collapse,#quarto-content.page-columns #quarto-sidebar.collapse{z-index:1055}}#quarto-content.page-columns main.column-page,#quarto-content.page-columns main.column-page-right,#quarto-content.page-columns main.column-page-left{z-index:0}.page-columns 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t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

',t.appendChild(t.resizeSensor),{fixed:1,absolute:1}[function(t,e){return t.currentStyle?t.currentStyle[e]:window.getComputedStyle?window.getComputedStyle(t,null).getPropertyValue(e):t.style[e]}(t,"position")]||(t.style.position="relative");var i,o,s=t.resizeSensor.childNodes[0],a=s.childNodes[0],u=t.resizeSensor.childNodes[1],c=(u.childNodes[0],function(){a.style.width=s.offsetWidth+10+"px",a.style.height=s.offsetHeight+10+"px",s.scrollLeft=s.scrollWidth,s.scrollTop=s.scrollHeight,u.scrollLeft=u.scrollWidth,u.scrollTop=u.scrollHeight,i=t.offsetWidth,o=t.offsetHeight});c();var d=function(t,e,n){t.attachEvent?t.attachEvent("on"+e,n):t.addEventListener(e,n)},f=function(){t.offsetWidth==i&&t.offsetHeight==o||t.resizedAttached&&t.resizedAttached.call(),c()};d(s,"scroll",f),d(u,"scroll",f)}var i=Object.prototype.toString.call(t),o="[object Array]"===i||"[object NodeList]"===i||"[object HTMLCollection]"===i||"undefined"!=typeof jQuery&&t instanceof jQuery||"undefined"!=typeof Elements&&t instanceof Elements;if(o)for(var s=0,a=t.length;s0;return this.updateCachedDimensions().then(function(){e&&t._resizeHandlers.forEach(function(e){e(t)})})}),t.define("loadDocument",function(t){var e=new a;return this.initialized?this.iframeEl.src?u.reject(new Error("widget already loaded")):(this.iframeEl.addEventListener("load",e.resolve,!1),this.iframeEl.addEventListener("error",e.reject,!1),this.iframeEl.src=t,e.promise):u.reject(new Error("sandbox not initialized"))}),t.after("initialize",function(){var t=new a,e=new a;this._iframe=null,this._iframeVersion=null,this._width=this._height=0,this._resizeHandlers=[],this._rendered=t,this._results=e,this._waitToSwapUntilRendered=!1}),t.override("insert",function(t,e,n,i){var a=this;return e=d.aug({id:t},f,e),n=d.aug({},l,n),this._iframe=s(e,n),p[t]=this,a._waitToSwapUntilRendered||this.onResize(o(function(){a.makeVisible()})),r.write(c(i,null,this._iframe))}),t.override("onResize",function(t){this._resizeHandlers.push(t)}),t.after("styleSelf",function(){return this.updateCachedDimensions()})}},function(t,e,n){var r=n(1),i=n(118),o=n(120),s=n(22),a=n(5),u=n(121);t.exports=function(t,e,n,c,d){function f(t){var e=u(this);s.trigger(t.type,{target:e,region:t.region,type:t.type,data:t.data||{}})}function l(e){var n=u(this),r=n&&n.id,i=a.asInt(e.width),o=a.asInt(e.height);r&&void 0!==i&&void 0!==o&&t(r,i,o)}(new i).attachReceiver(new o.Receiver(r,"twttr.button")).bind("twttr.private.trigger",f).bind("twttr.private.resizeButton",l),(new i).attachReceiver(new o.Receiver(r,"twttr.embed")).bind("twttr.private.initialized",function(t){var e=u(this),n=e&&e.id,r=t.iframe_version;n&&r&&c&&c(n,r)}).bind("twttr.private.trigger",f).bind("twttr.private.results",function(){var t=u(this),n=t&&t.id;n&&e&&e(n)}).bind("twttr.private.rendered",function(){var t=u(this),e=t&&t.id;e&&n&&n(e)}).bind("twttr.private.no_results",function(){var t=u(this),e=t&&t.id;e&&d&&d(e)}).bind("twttr.private.resize",l)}},function(t,e,n){var r=n(24),i=n(119),o=n(0),s=n(6),a=n(23),u="2.0";function c(t){this.registry=t||{}}function d(t){var e,n;return e=o.isType("string",t),n=o.isType("number",t),e||n||null===t}function f(t,e){return{jsonrpc:u,id:d(t)?t:null,error:e}}c.prototype._invoke=function(t,e){var n,r,i;n=this.registry[t.method],r=t.params||[],r=o.isType("array",r)?r:[r];try{i=n.apply(e.source||null,r)}catch(t){i=s.reject(t.message)}return a.isPromise(i)?i:s.resolve(i)},c.prototype._processRequest=function(t,e){var n,r;return function(t){var e,n,r;return!!o.isObject(t)&&(e=t.jsonrpc===u,n=o.isType("string",t.method),r=!("id"in t)||d(t.id),e&&n&&r)}(t)?(n="params"in t&&(r=t.params,!o.isObject(r)||o.isType("function",r))?s.resolve(f(t.id,i.INVALID_PARAMS)):this.registry[t.method]?this._invoke(t,{source:e}).then(function(e){return n=t.id,{jsonrpc:u,id:n,result:e};var n},function(){return f(t.id,i.INTERNAL_ERROR)}):s.resolve(f(t.id,i.METHOD_NOT_FOUND)),null!=t.id?n:s.resolve()):s.resolve(f(t.id,i.INVALID_REQUEST))},c.prototype.attachReceiver=function(t){return t.attachTo(this),this},c.prototype.bind=function(t,e){return this.registry[t]=e,this},c.prototype.receive=function(t,e){var n,a,u,c=this;try{u=t,t=o.isType("string",u)?r.parse(u):u}catch(t){return s.resolve(f(null,i.PARSE_ERROR))}return e=e||null,a=((n=o.isType("array",t))?t:[t]).map(function(t){return c._processRequest(t,e)}),n?function(t){return s.all(t).then(function(t){return(t=t.filter(function(t){return void 0!==t})).length?t:void 0})}(a):a[0]},t.exports=c},function(t){t.exports={PARSE_ERROR:{code:-32700,message:"Parse error"},INVALID_REQUEST:{code:-32600,message:"Invalid 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The default, with method = 1, uses the same method as algorithm 2 in Ogata (1981), simulating until a given time horizon (set by argument n).

sim <- sim_hawkes(mu = 0.5, alpha = 1, beta = 1.5, n = 40, plot = TRUE)
-
-

-

The second option, setting method = 2, uses an accept/reject framework and the argument n specifies the number of points to simulate (default n = 100).

sim <- sim_hawkes(mu = 0.5, alpha = 1, beta = 1.5, plot = TRUE, method = 2)
-
-

-

2.2 Fitting a univariate Hawkes model

-
devtools::load_all("~/Git/stelfi")
-
-
ℹ Loading stelfi
-
-
## require(stelfi)
+
require(stelfi)

To fit a univariate Hawkes model in stelfi use the function fit_hawkes() with the following required arguments

    @@ -318,15 +308,15 @@

    2.2.1 A simulated example

    Simulating a realisation of a univariate Hawkes process (see Section 2.1 for more details) with \(\mu = 1.3\), \(\alpha = 0.4\), and \(\beta = 1.5\) (over \(t \in [0, 500]\)).

    -
    times <- sim_hawkes(mu = 1.3, alpha = 0.4, beta = 1.5, n = 500)
    +
    times <- sim_hawkes(mu = 1.3, alpha = 0.4, beta = 1.5, n = 500)
    -
    ## starting values
    -sv <- c(mu = 1.3, alpha = 0.4, beta = 1.5)
    -## using stelfi
    -fit <- fit_hawkes(times = times, parameters = sv)
    -stelfi <- get_coefs(fit)
    -stelfi
    +
    ## starting values
    +sv <- c(mu = 1.3, alpha = 0.4, beta = 1.5)
    +## using stelfi
    +fit <- fit_hawkes(times = times, parameters = sv)
    +stelfi <- get_coefs(fit)
    +stelfi
           Estimate Std. Error
     mu    1.3997320  0.1125668
    @@ -336,27 +326,27 @@ 

    As a comparison, below emhawkes (Lee (2023)), and hawkesbow (Cheysson (2021)) are used to fit a univariate Hawkes process to the same simulated data.

    -
    ## benchmark using emhawkes
    -require(emhawkes)
    -h <- new("hspec", mu = sv[1], alpha = sv[2], beta = sv[3])
    -## emhawkes requires the inter arrival times to fit the model
    -inter <- diff(times)
    -fit_em <- hfit(object = h, inter_arrival = inter)
    -em <- summary(fit_em)$estimate
    -em
    +
    ## benchmark using emhawkes
    +require(emhawkes)
    +h <- new("hspec", mu = sv[1], alpha = sv[2], beta = sv[3])
    +## emhawkes requires the inter arrival times to fit the model
    +inter <- diff(times)
    +fit_em <- hfit(object = h, inter_arrival = inter)
    +em <- summary(fit_em)$estimate
    +em
            Estimate Std. error   t value      Pr(> t)
     mu1    1.3976553  0.1091437 12.805644 1.524557e-37
     alpha1 0.3683585  0.1380857  2.667608 7.639335e-03
     beta1  1.7380101  0.8236957  2.110015 3.485708e-02
    -
    ## bench mark using hawkesbow
    -require(hawkesbow)
    -fit_bow <- mle(events = times, kern = "Exponential", end = max(times))
    -## use the Hessian to obtain the standard errors from hawkesbow
    -bow <- cbind(Estimate = fit_bow$par,
    -         "Std. error" = -fit_bow$model$ddloglik(times, max(times)) |> solve() |> diag() |> sqrt())
    -bow
    +
    ## bench mark using hawkesbow
    +require(hawkesbow)
    +fit_bow <- mle(events = times, kern = "Exponential", end = max(times))
    +## use the Hessian to obtain the standard errors from hawkesbow
    +bow <- cbind(Estimate = fit_bow$par,
    +         "Std. error" = -fit_bow$model$ddloglik(times, max(times)) |> solve() |> diag() |> sqrt())
    +bow
          Estimate Std. error
     [1,] 1.3998368 0.11238176
    @@ -422,12 +412,12 @@ 

    2.2.2 An applied example

    A NIWA scientist found a working USB in the scat of a leopard seal, they then tweeted about it in the hopes of finding its owner1. The dates and times of these tweets and retweets are available in stelfi as retweets_niwa.

    -
    data(retweets_niwa)
    +
    data(retweets_niwa)

    The dates/times need to be numeric and sorted in ascending order (starting a time \(t = 0\)). Note too that there can be no simultaneous events.

    -
    ## numeric time stamps
    -times <- unique(sort(as.numeric(difftime(retweets_niwa ,min(retweets_niwa),units = "mins"))))
    +
    ## numeric time stamps
    +times <- unique(sort(as.numeric(difftime(retweets_niwa ,min(retweets_niwa),units = "mins"))))

    The histogram below shows the observed counts (4772) of unique retweet times from the original tweet (\(t=0\)) to the time (just over two days later) that the owner of the USB came forward, t = 3143 mins.

    @@ -436,14 +426,14 @@

    To fit the model chose some starting values for the parameters and supply these along with the numeric times to the function fit_hawkes().

    -
    -
    sv <- c(mu = 9, alpha = 3, beta = 10)
    -fit <- fit_hawkes(times = times, parameters = sv) 
    +
    +
    sv <- c(mu = 9, alpha = 3, beta = 10)
    +fit <- fit_hawkes(times = times, parameters = sv) 
    -
    ## print out estimated parameters
    -pars <- get_coefs(fit)
    -pars
    +
    ## print out estimated parameters
    +pars <- get_coefs(fit)
    +pars
            Estimate  Std. Error
     mu    0.06328099 0.017783908
    @@ -460,7 +450,7 @@ 

    The show_hawkes() function can be called on the fitted model object to plot the estimated conditional intensity and the data, top and bottom panels below respectively.

    -
    show_hawkes(fit)
    +
    show_hawkes(fit)

    @@ -474,11 +464,11 @@

    The random change theorem (Daley, Vere-Jones, et al. (2003)) states that if a set of events \([\tau_1, ..., \tau_n]\) is a realisation from a inhomogeneous Poisson process then \([\Lambda(\tau_1), ..., \Lambda(\tau_n)]\) is a realisation of a homogeneous Poisson process with unit rate. Letting \(\delta \Lambda_{i} = \Lambda(\tau_i) - \Lambda(\tau_{i-1})\) for \(i = 2, ..., N\) and \(\delta \Lambda_{1} = \Lambda(\tau_1)\), under the theorem above \(\delta \Lambda_{i} \sim \text{Exp}(1)\). Using this result a typical goodness-of-fit test is a Kolmogorov-Smirnov (KS) test (see Daley, Vere-Jones, et al. (2003) for more details) where the KS statistic is a measure of the distance between the empirical distribution of all \(\delta \Lambda_{i}\) and the CDF of a \(\text{Exp}(1)\) distribution.

    The compensator differences can be extracted from a fitted model using the compensator_differences() function, and a KS test can be carried out manually:

    -
    times <- sim_hawkes(mu = 1.3, alpha = 0.4, beta = 1.5, n = 500)
    -sv <- c(mu = 2, alpha = 1, beta = 5)
    -fit <- fit_hawkes(times = times, parameters = sv)
    -compensator <- compensator_differences(fit)
    -stats::ks.test(compensator, "pexp")
    +
    times <- sim_hawkes(mu = 1.3, alpha = 0.4, beta = 1.5, n = 500)
    +sv <- c(mu = 2, alpha = 1, beta = 5)
    +fit <- fit_hawkes(times = times, parameters = sv)
    +compensator <- compensator_differences(fit)
    +stats::ks.test(compensator, "pexp")
    
         Asymptotic one-sample Kolmogorov-Smirnov test
    @@ -491,7 +481,7 @@ 

    This gives no evidence against the compensator values coming from a \(\text{Exp}(1)\) distribution.

    Another goodness-of-fit test is the Box-Ljung (or Ljung–Box) test, which tests for autocorrelation between the consecutive compensator values (i.e., independence/stationarity).

    -
    stats::Box.test(compensator, type = "Ljung")
    +
    stats::Box.test(compensator, type = "Ljung")
    
         Box-Ljung test
    @@ -509,7 +499,7 @@ 

    bottom right, consecutive compensator differences, which should show no obvious pattern (no autocorrelation evident) under a well fitting model.

-
show_hawkes_GOF(fit)
+
show_hawkes_GOF(fit)

     Asymptotic one-sample Kolmogorov-Smirnov test
@@ -534,43 +524,43 @@ 

\(\mu = \mu(t)\) in Equation 2.1 (i.e., the baseline is time varying, rather than being constant).

Below we simulate data from a Hawkes process with \(\mu(t) = [A + B*\text{sin}(\frac{2 \pi t}{365.25})]\) (with \(A = 1\) and \(B = 0.5\)) to represent a yearly cycle and self-exciting parameters \(\alpha = 1\) and \(\beta = 2\) using hawkesbow (Cheysson (2021)).

-
mut <- function(t) {
- 1 + 0.5*sin((2*pi*t)/365.25)
-}
+
mut <- function(t) {
+ 1 + 0.5*sin((2*pi*t)/365.25)
+}
-
set.seed(1)
-times <- hawkesbow::hawkes(1000, fun = mut,
- M = 1.5, repr = 0.5, family = "exp", rate = 2)$p
+
set.seed(1)
+times <- hawkesbow::hawkes(1000, fun = mut,
+ M = 1.5, repr = 0.5, family = "exp", rate = 2)$p

To fit this model in stelfi the function \(\mu(t)\) (background) and its integral \(\int_0^t \mu(y) dy\) (background_integral) are supplied by the user. To ensure \(\mu(t) > 0\) below \(\mu(t)\) is written as a function of \(x\) and \(y\), where \(A = e^x\) and \(B = \text{logit}(y) e^x\).

-
background <- function(params,t){
-        A = exp(params[[1]])
-        B = stats::plogis(params[[2]]) * A
-        return(A + B*sin((2*pi*t)/365.25))
-}
-
-background_integral <- function(params,x){
-        A = exp(params[[1]])
-        B = stats::plogis(params[[2]]) * A
-        return((A*x)-B*cos((2*pi*x)/365.25))
-}
+
background <- function(params,t){
+        A = exp(params[[1]])
+        B = stats::plogis(params[[2]]) * A
+        return(A + B*sin((2*pi*t)/365.25))
+}
+
+background_integral <- function(params,x){
+        A = exp(params[[1]])
+        B = stats::plogis(params[[2]]) * A
+        return((A*x)-B*cos((2*pi*x)/365.25))
+}

These functions are then passed to the function fit_hawkes_cbf() alongside the observed times (times) and a list of starting values for both \(\mu(t)\) and the self-exciting components via the argument background_parameters and parameters respectively.

-
sv = list(alpha = 0.5, beta = 1.5)
-background_sv = list(1,1)
-fit <- fit_hawkes_cbf(times = times, parameters = sv,
-background = background, background_integral = background_integral, background_parameters = background_sv)
+
sv = list(alpha = 0.5, beta = 1.5)
+background_sv = list(1,1)
+fit <- fit_hawkes_cbf(times = times, parameters = sv,
+background = background, background_integral = background_integral, background_parameters = background_sv)

The estimated values of the the transformed \(A\) and \(B\) parameters are returned via fit$background_parameters, the code below transforms them back to the original scale.

-
exp(fit$background_parameters[1])
+
exp(fit$background_parameters[1])
[1] 1.135523
-
plogis(fit$background_parameters[2]) * exp(fit$background_parameters[1])
+
plogis(fit$background_parameters[2]) * exp(fit$background_parameters[1])
[1] 0.7755937
@@ -609,14 +599,14 @@

-
show_hawkes(fit)
+
show_hawkes(fit)

The function show_hawkes_GOF() will show/print the model diagnostics discussed in Section 2.3.

-
show_hawkes_GOF(fit)
+
show_hawkes_GOF(fit)

     Asymptotic one-sample Kolmogorov-Smirnov test
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