diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
index 1947ea3..0000000
Binary files a/.DS_Store and /dev/null differ
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index b811cc6..fa262d0 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -1,20 +1,12 @@
-# NOTE: This workflow is overkill for most R packages
-# check-standard.yaml is likely a better choice
-# usethis::use_github_action("check-standard") will install it.
-#
-# For help debugging build failures open an issue on the RStudio community with the 'github-actions' tag.
-# https://community.rstudio.com/new-topic?category=Package%20development&tags=github-actions
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   schedule:
     - cron: '0 0 1 * *'
   push:
-    branches:
-      - main
-      - master
+    branches: [main, master]
   pull_request:
-    branches:
-      - main
-      - master
+    branches: [main, master]
 
 name: R-CMD-check
 
@@ -28,78 +20,32 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - {os: macOS-latest,   r: 'release'}
+          - {os: macos-latest,   r: 'release'}
           - {os: windows-latest, r: 'release'}
-          # - {os: windows-latest, r: '3.6'}
-          - {os: ubuntu-18.04,   r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest", http-user-agent: "R/4.0.0 (ubuntu-18.04) R (4.0.0 x86_64-pc-linux-gnu x86_64 linux-gnu) on GitHub Actions" }
-          - {os: ubuntu-18.04,   r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
-          # - {os: ubuntu-18.04,   r: 'oldrel',  rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
-          # - {os: ubuntu-18.04,   r: '3.6',     rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
+          - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,   r: 'release'}
+          - {os: ubuntu-latest,   r: 'oldrel-1'}
 
     env:
-      RSPM: ${{ matrix.config.rspm }}
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
-      - uses: r-lib/actions/setup-r@v1
-        id: install-r
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
           http-user-agent: ${{ matrix.config.http-user-agent }}
+          use-public-rspm: true
 
-      - uses: r-lib/actions/setup-pandoc@v1
-
-      - name: Install pak and query dependencies
-        run: |
-          install.packages("pak", repos = "https://r-lib.github.io/p/pak/dev/")
-          saveRDS(pak::pkg_deps("local::.", dependencies = TRUE), ".github/r-depends.rds")
-        shell: Rscript {0}
-
-      - name: Restore R package cache
-        uses: actions/cache@v2
+      - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          path: ${{ env.R_LIBS_USER }}
-          key: ${{ matrix.config.os }}-${{ steps.install-r.outputs.installed-r-version }}-1-${{ hashFiles('.github/r-depends.rds') }}
-          restore-keys: ${{ matrix.config.os }}-${{ steps.install-r.outputs.installed-r-version }}-1-
-
-      - name: Install system dependencies
-        if: runner.os == 'Linux'
-        run: |
-          pak::local_system_requirements(execute = TRUE)
-          pak::pkg_system_requirements("rcmdcheck", execute = TRUE)
-        shell: Rscript {0}
-
-      - name: Install dependencies
-        run: |
-          pak::local_install_dev_deps(upgrade = TRUE)
-          pak::pkg_install("rcmdcheck")
-        shell: Rscript {0}
-
-      - name: Session info
-        run: |
-          options(width = 100)
-          pkgs <- installed.packages()[, "Package"]
-          sessioninfo::session_info(pkgs, include_base = TRUE)
-        shell: Rscript {0}
-
-      - name: Check
-        env:
-          _R_CHECK_CRAN_INCOMING_: false
-        run: |
-          options(crayon.enabled = TRUE)
-          rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check")
-        shell: Rscript {0}
-
-      - name: Show testthat output
-        if: always()
-        run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true
-        shell: bash
+          extra-packages: any::rcmdcheck
+          needs: check
 
-      - name: Upload check results
-        if: failure()
-        uses: actions/upload-artifact@main
+      - uses: r-lib/actions/check-r-package@v2
         with:
-          name: ${{ matrix.config.os }}-r${{ matrix.config.r }}-results
-          path: check
+          upload-snapshots: true
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index 3eb8dd9..ed7650c 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -1,57 +1,48 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
-    branches:
-      - main
-      - master
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
 
 name: pkgdown
 
 jobs:
   pkgdown:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
     env:
-      RSPM: https://packagemanager.rstudio.com/cran/__linux__/bionic/latest
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
-
+    permissions:
+      contents: write
     steps:
-      - uses: actions/checkout@v2
-
-      - uses: r-lib/actions/setup-r@v1
-        id: install-r
+      - uses: actions/checkout@v3
 
-      - uses: r-lib/actions/setup-pandoc@v1
-
-      - name: Install pak and query dependencies
-        run: |
-          install.packages("pak", repos = "https://r-lib.github.io/p/pak/dev/")
-          saveRDS(pak::pkg_deps("local::.", dependencies = TRUE), ".github/r-depends.rds")
-        shell: Rscript {0}
+      - uses: r-lib/actions/setup-pandoc@v2
 
-      - name: Restore R package cache
-        uses: actions/cache@v2
+      - uses: r-lib/actions/setup-r@v2
         with:
-          path: ${{ env.R_LIBS_USER }}
-          key: ubuntu-18.04-${{ steps.install-r.outputs.installed-r-version }}-1-${{ hashFiles('.github/r-depends.rds') }}
-          restore-keys: ubuntu-18.04-${{ steps.install-r.outputs.installed-r-version }}-1-
+          use-public-rspm: true
 
-      - name: Install system dependencies
-        if: runner.os == 'Linux'
-        run: |
-          pak::local_system_requirements(execute = TRUE)
-          pak::pkg_system_requirements("pkgdown", execute = TRUE)
-        shell: Rscript {0}
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
 
-      - name: Install dependencies
-        run: |
-          pak::local_install_dev_deps(upgrade = TRUE)
-          pak::pkg_install("pkgdown")
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
         shell: Rscript {0}
 
-      - name: Install package
-        run: R CMD INSTALL .
-
-      - name: Build and deploy pkgdown site
-        run: |
-          git config --local user.name "$GITHUB_ACTOR"
-          git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com"
-          Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)'
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/github-pages-deploy-action@v4.4.1
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs
diff --git a/.github/workflows/pr-commands.yaml b/.github/workflows/pr-commands.yaml
index e5edf9f..71f335b 100644
--- a/.github/workflows/pr-commands.yaml
+++ b/.github/workflows/pr-commands.yaml
@@ -1,55 +1,79 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   issue_comment:
     types: [created]
+
 name: Commands
+
 jobs:
   document:
-    if: startsWith(github.event.comment.body, '/document')
+    if: ${{ github.event.issue.pull_request && (github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'OWNER') && startsWith(github.event.comment.body, '/document') }}
     name: document
-    runs-on: macOS-latest
+    runs-on: ubuntu-latest
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v2
-      - uses: r-lib/actions/pr-fetch@v1
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/pr-fetch@v2
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
-      - uses: r-lib/actions/setup-r@v1
-      - name: Install dependencies
-        run: Rscript -e 'install.packages(c("remotes", "roxygen2"))' -e 'remotes::install_deps(dependencies = TRUE)'
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::roxygen2
+          needs: pr-document
+
       - name: Document
-        run: Rscript -e 'roxygen2::roxygenise()'
+        run: roxygen2::roxygenise()
+        shell: Rscript {0}
+
       - name: commit
         run: |
-          git config --local user.email "actions@github.com"
-          git config --local user.name "GitHub Actions"
+          git config --local user.name "$GITHUB_ACTOR"
+          git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com"
           git add man/\* NAMESPACE
           git commit -m 'Document'
-      - uses: r-lib/actions/pr-push@v1
+
+      - uses: r-lib/actions/pr-push@v2
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
+
   style:
-    if: startsWith(github.event.comment.body, '/style')
+    if: ${{ github.event.issue.pull_request && (github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'OWNER') && startsWith(github.event.comment.body, '/style') }}
     name: style
-    runs-on: macOS-latest
+    runs-on: ubuntu-latest
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v2
-      - uses: r-lib/actions/pr-fetch@v1
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/pr-fetch@v2
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
-      - uses: r-lib/actions/setup-r@v1
+
+      - uses: r-lib/actions/setup-r@v2
+
       - name: Install dependencies
-        run: Rscript -e 'install.packages("styler")'
+        run: install.packages("styler")
+        shell: Rscript {0}
+
       - name: Style
-        run: Rscript -e 'styler::style_pkg()'
+        run: styler::style_pkg()
+        shell: Rscript {0}
+
       - name: commit
         run: |
-          git config --local user.email "actions@github.com"
-          git config --local user.name "GitHub Actions"
+          git config --local user.name "$GITHUB_ACTOR"
+          git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com"
           git add \*.R
           git commit -m 'Style'
-      - uses: r-lib/actions/pr-push@v1
+
+      - uses: r-lib/actions/pr-push@v2
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
index 965cd84..27d4528 100644
--- a/.github/workflows/test-coverage.yaml
+++ b/.github/workflows/test-coverage.yaml
@@ -1,54 +1,50 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
-    branches:
-      - main
-      - master
+    branches: [main, master]
   pull_request:
-    branches:
-      - main
-      - master
+    branches: [main, master]
 
 name: test-coverage
 
 jobs:
   test-coverage:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-latest
     env:
-      RSPM: https://packagemanager.rstudio.com/cran/__linux__/bionic/latest
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
-      - uses: r-lib/actions/setup-r@v1
-        id: install-r
-
-      - name: Install pak and query dependencies
-        run: |
-          install.packages("pak", repos = "https://r-lib.github.io/p/pak/dev/")
-          saveRDS(pak::pkg_deps("local::.", dependencies = TRUE), ".github/r-depends.rds")
-        shell: Rscript {0}
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
 
-      - name: Restore R package cache
-        uses: actions/cache@v2
+      - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          path: ${{ env.R_LIBS_USER }}
-          key: ubuntu-18.04-${{ steps.install-r.outputs.installed-r-version }}-1-${{ hashFiles('.github/r-depends.rds') }}
-          restore-keys: ubuntu-18.04-${{ steps.install-r.outputs.installed-r-version }}-1-
+          extra-packages: any::covr
+          needs: coverage
 
-      - name: Install system dependencies
-        if: runner.os == 'Linux'
+      - name: Test coverage
         run: |
-          pak::local_system_requirements(execute = TRUE)
-          pak::pkg_system_requirements("covr", execute = TRUE)
+          covr::codecov(
+            quiet = FALSE,
+            clean = FALSE,
+            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
+          )
         shell: Rscript {0}
 
-      - name: Install dependencies
+      - name: Show testthat output
+        if: always()
         run: |
-          pak::local_install_dev_deps(upgrade = TRUE)
-          pak::pkg_install("covr")
-        shell: Rscript {0}
+          ## --------------------------------------------------------------------
+          find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true
+        shell: bash
 
-      - name: Test coverage
-        run: covr::codecov()
-        shell: Rscript {0}
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v3
+        with:
+          name: coverage-test-failures
+          path: ${{ runner.temp }}/package
diff --git a/.gitignore b/.gitignore
index c833a2c..86fa0f8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,4 @@
 .RData
 .Ruserdata
 inst/doc
+.DS_Store
diff --git a/DESCRIPTION b/DESCRIPTION
index 9b71ca1..f79a2d1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: modeltime.ensemble
 Type: Package
 Title: Ensemble Algorithms for Time Series Forecasting with Modeltime
-Version: 1.0.3
+Version: 1.0.3.9000
 Authors@R: c(
     person("Matt", "Dancho", email = "mdancho@business-science.io", role = c("aut", "cre")),
     person("Business Science", role = "cph")
@@ -11,7 +11,7 @@ Description:
     weighted averaging, and stacking. These techniques are popular methods 
     to improve forecast accuracy and stability. Refer to papers such as 
     "Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) <doi:10.3390>.
-URL: https://github.com/business-science/modeltime.ensemble
+URL: https://business-science.github.io/modeltime.ensemble/, https://github.com/business-science/modeltime.ensemble
 BugReports: https://github.com/business-science/modeltime.ensemble/issues
 License: MIT + file LICENSE
 Encoding: UTF-8
@@ -22,7 +22,7 @@ Depends:
 Imports: 
     tune (>= 0.1.2),
     rsample,
-    yardstick, 
+    yardstick,
     workflows (>= 0.2.1),
     parsnip (>= 0.1.6),
     recipes (>= 0.1.15),
@@ -31,7 +31,6 @@ Imports:
     dplyr (>= 1.0.0),
     tidyr,
     purrr,
-    glue,
     stringr,
     rlang (>= 0.1.2),
     cli,
@@ -41,25 +40,18 @@ Imports:
     parallel, 
     doParallel,
     foreach,
-Suggests: 
+Suggests:
     gt,
-    crayon,
     dials,
     glmnet,
-    progressr,
     utils,
-    roxygen2,
     earth,
     testthat,
     tidymodels,
     xgboost,
-    tidyverse,
     lubridate,
     knitr,
-    rmarkdown,
-    covr, 
-    qpdf,
-    remotes
+    rmarkdown
 RoxygenNote: 7.2.3
 VignetteBuilder: knitr
 Roxygen: list(markdown = TRUE)
diff --git a/NEWS.md b/NEWS.md
index 17f9c2a..197d8e0 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,5 @@
+# modeltime.ensemble (development version)
+
 # modeltime.ensemble 1.0.3
 
 - Resubmit to CRAN  (following timetk archival)
diff --git a/R/ensemble_average.R b/R/ensemble_average.R
index eef0008..cf020b5 100644
--- a/R/ensemble_average.R
+++ b/R/ensemble_average.R
@@ -26,7 +26,7 @@
 #' library(tidymodels)
 #' library(modeltime)
 #' library(modeltime.ensemble)
-#' library(tidyverse)
+#' library(dplyr)
 #' library(timetk)
 #'
 #' # Make an ensemble from a Modeltime Table
@@ -89,14 +89,12 @@ print.mdl_time_ensemble_avg <- function(x, ...) {
     print(cli::rule("Modeltime Ensemble", width = min(65, cli::console_width())))
 
     if (x$parameters$type == "mean") {
-        msg <- glue::glue("Ensemble of {x$n_models} Models (MEAN)")
+        msg <- cli::format_inline("Ensemble of {x$n_models} Models (MEAN)")
     } else {
-        msg <- glue::glue("Ensemble of {x$n_models} Models (MEDIAN)")
+        msg <- cli::format_inline("Ensemble of {x$n_models} Models (MEDIAN)")
     }
 
-    print(msg)
-
-    cli::cat_line()
+    cat(msg, "\n\n")
 
     print(x$model_tbl)
 
diff --git a/R/ensemble_model_spec.R b/R/ensemble_model_spec.R
index 843fcdb..c3f0726 100644
--- a/R/ensemble_model_spec.R
+++ b/R/ensemble_model_spec.R
@@ -83,7 +83,7 @@
 #' library(tidymodels)
 #' library(modeltime)
 #' library(modeltime.ensemble)
-#' library(tidyverse)
+#' library(dplyr)
 #' library(timetk)
 #'
 #' # Step 1: Make resample predictions for submodels
@@ -146,7 +146,7 @@ ensemble_model_spec <- function(object,
 
     if (rlang::is_missing(model_spec)) rlang::abort("'model_spec' must be provided. Try creating a model_spec using parsnip or modeltime models.")
     if (!inherits(model_spec, "model_spec")) rlang::abort("'model_spec' must be a `model_spec` object. Try creating a model_spec using parsnip or modeltime models.")
-    if (is.null(model_spec$engine)) rlang::abort("'model_spec' does not have an engine set. Try setting an engine using `parsnip::set_engine()`.")
+    if (is.null(model_spec$engine)) cli::cli_abort("'model_spec' does not have an engine set. Try setting an engine using {.fn parsnip::set_engine}.")
 
 
     if (nrow(object) < 2) rlang::abort("An ensemble requires two or more models in the Modeltime Table.")
@@ -213,11 +213,9 @@ print.mdl_time_ensemble_model_spec <- function(x, ...) {
     print(cli::rule("Modeltime Ensemble", width = min(65, cli::console_width())))
 
     desc <- x$fit$fit %>% modeltime::get_model_description()
-    msg  <- glue::glue("Ensemble of {x$n_models} Models ({stringr::str_c(desc, ' STACK')})")
+    msg  <- cli::format_inline("Ensemble of {x$n_models} Models ({stringr::str_c(desc, ' STACK')})")
 
-    print(msg)
-
-    cli::cat_line()
+    cat(msg, "\n\n")
 
     print(x$model_tbl)
 
diff --git a/R/ensemble_weighted.R b/R/ensemble_weighted.R
index 6c2431b..3bd20b0 100644
--- a/R/ensemble_weighted.R
+++ b/R/ensemble_weighted.R
@@ -27,7 +27,7 @@
 #' library(tidymodels)
 #' library(modeltime)
 #' library(modeltime.ensemble)
-#' library(tidyverse)
+#' library(dplyr)
 #' library(timetk)
 #'
 #' # Make an ensemble from a Modeltime Table
@@ -117,11 +117,9 @@ print.mdl_time_ensemble_wt <- function(x, ...) {
 
     print(cli::rule("Modeltime Ensemble", width = min(65, cli::console_width())))
 
-    msg <- glue::glue("Ensemble of {x$n_models} Models (WEIGHTED)")
+    msg <- cli::format_inline("Ensemble of {x$n_models} Models (WEIGHTED)")
 
-    print(msg)
-
-    cli::cat_line()
+    cat(msg, "\n\n")
 
     print(dplyr::left_join(x$model_tbl, x$fit$loadings_tbl, by = ".model_id"))
 
diff --git a/R/nested_ensemble_average.R b/R/nested_ensemble_average.R
index a4e8f39..7022745 100644
--- a/R/nested_ensemble_average.R
+++ b/R/nested_ensemble_average.R
@@ -336,7 +336,7 @@ ensemble_nested_average_parallel <- function(object,
         utils::capture.output() %>%
         stringr::str_remove("Time difference of ")
 
-    if (control$verbose) cli::cli_inform(stringr::str_glue("Finished in: {time_elapsed}."))
+    if (control$verbose) cli::cli_inform("Finished in: {time_elapsed}.")
 
     # STRUCTURE ----
 
diff --git a/R/tibble-type_sum.R b/R/tibble-type_sum.R
index f7de535..3e32b66 100644
--- a/R/tibble-type_sum.R
+++ b/R/tibble-type_sum.R
@@ -2,7 +2,7 @@
 #' @importFrom tibble type_sum
 #' @export
 type_sum.mdl_time_ensemble <- function(x) {
-    glue::glue("ensemble [{x$n_models}]")
+    cli::cli_text("ensemble [{x$n_models}]")
 }
 
 
diff --git a/README.Rmd b/README.Rmd
index b869bb3..96b2526 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -23,8 +23,9 @@ knitr::opts_chunk$set(
 [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/modeltime.ensemble)](https://cran.r-project.org/package=modeltime.ensemble)
 ![](http://cranlogs.r-pkg.org/badges/modeltime.ensemble?color=brightgreen)
 ![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime.ensemble?color=brightgreen)
-[![R-CMD-check](https://github.com/business-science/modeltime.ensemble/workflows/R-CMD-check/badge.svg)](https://github.com/business-science/modeltime.ensemble/actions)
+[![R-CMD-check](https://github.com/business-science/modeltime.ensemble/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/business-science/modeltime.ensemble/actions/workflows/R-CMD-check.yaml)
 [![Codecov test coverage](https://codecov.io/gh/business-science/modeltime.ensemble/branch/master/graph/badge.svg)](https://app.codecov.io/gh/business-science/modeltime.ensemble?branch=master)
+
 <!-- badges: end -->
 
 
@@ -64,7 +65,7 @@ Load the following libraries.
 library(tidymodels)
 library(modeltime)
 library(modeltime.ensemble)
-library(tidyverse)
+library(dplyr)
 library(timetk)
 ```
 
diff --git a/README.md b/README.md
index 9dfcf4b..48e8105 100644
--- a/README.md
+++ b/README.md
@@ -5,12 +5,13 @@
 
 <!-- badges: start -->
 
-[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/modeltime.ensemble)](https://cran.r-project.org/package=modeltime.ensemble)
+[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/modeltime.ensemble)](https://cran.r-project.org/package=modeltime.ensemble)
 ![](http://cranlogs.r-pkg.org/badges/modeltime.ensemble?color=brightgreen)
 ![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime.ensemble?color=brightgreen)
-[![R-CMD-check](https://github.com/business-science/modeltime.ensemble/workflows/R-CMD-check/badge.svg)](https://github.com/business-science/modeltime.ensemble/actions)
+[![R-CMD-check](https://github.com/business-science/modeltime.ensemble/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/business-science/modeltime.ensemble/actions/workflows/R-CMD-check.yaml)
 [![Codecov test
 coverage](https://codecov.io/gh/business-science/modeltime.ensemble/branch/master/graph/badge.svg)](https://app.codecov.io/gh/business-science/modeltime.ensemble?branch=master)
+
 <!-- badges: end -->
 
 > Ensemble Algorithms for Time Series Forecasting with Modeltime
@@ -51,7 +52,7 @@ Load the following libraries.
 library(tidymodels)
 library(modeltime)
 library(modeltime.ensemble)
-library(tidyverse)
+library(dplyr)
 library(timetk)
 ```
 
@@ -80,7 +81,7 @@ ensemble_fit <- m750_models %>%
 
 ensemble_fit
 #> ── Modeltime Ensemble ───────────────────────────────────────────
-#> Ensemble of 3 Models (MEAN)
+#> Ensemble of 3 Models (MEAN) 
 #> 
 #> # Modeltime Table
 #> # A tibble: 3 × 3
@@ -130,23 +131,23 @@ The modeltime ecosystem is growing
 Modeltime is part of a **growing ecosystem** of Modeltime forecasting
 packages.
 
--   [Modeltime (Machine
-    Learning)](https://business-science.github.io/modeltime/)
+- [Modeltime (Machine
+  Learning)](https://business-science.github.io/modeltime/)
 
--   [Modeltime H2O
-    (AutoML)](https://business-science.github.io/modeltime.h2o/)
+- [Modeltime H2O
+  (AutoML)](https://business-science.github.io/modeltime.h2o/)
 
--   [Modeltime GluonTS (Deep
-    Learning)](https://business-science.github.io/modeltime.gluonts/)
+- [Modeltime GluonTS (Deep
+  Learning)](https://business-science.github.io/modeltime.gluonts/)
 
--   [Modeltime Ensemble (Blending
-    Forecasts)](https://business-science.github.io/modeltime.ensemble/)
+- [Modeltime Ensemble (Blending
+  Forecasts)](https://business-science.github.io/modeltime.ensemble/)
 
--   [Modeltime Resample
-    (Backtesting)](https://business-science.github.io/modeltime.resample/)
+- [Modeltime Resample
+  (Backtesting)](https://business-science.github.io/modeltime.resample/)
 
--   [Timetk (Feature Engineering, Data Wrangling, Time Series
-    Visualization)](https://business-science.github.io/timetk/)
+- [Timetk (Feature Engineering, Data Wrangling, Time Series
+  Visualization)](https://business-science.github.io/timetk/)
 
 ## Take the High-Performance Forecasting Course
 
@@ -176,17 +177,17 @@ Series Forecasting
 Course**](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting).
 You will learn:
 
--   **Time Series Machine Learning** (cutting-edge) with `Modeltime` -
-    30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
--   **Deep Learning** with `GluonTS` (Competition Winners)
--   **Time Series Preprocessing**, Noise Reduction, & Anomaly Detection
--   **Feature engineering** using lagged variables & external regressors
--   **Hyperparameter Tuning**
--   **Time series cross-validation**
--   **Ensembling** Multiple Machine Learning & Univariate Modeling
-    Techniques (Competition Winner)
--   **Scalable Forecasting** - Forecast 1000+ time series in parallel
--   and more.
+- **Time Series Machine Learning** (cutting-edge) with `Modeltime` - 30+
+  Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
+- **Deep Learning** with `GluonTS` (Competition Winners)
+- **Time Series Preprocessing**, Noise Reduction, & Anomaly Detection
+- **Feature engineering** using lagged variables & external regressors
+- **Hyperparameter Tuning**
+- **Time series cross-validation**
+- **Ensembling** Multiple Machine Learning & Univariate Modeling
+  Techniques (Competition Winner)
+- **Scalable Forecasting** - Forecast 1000+ time series in parallel
+- and more.
 
 <p class="text-center" style="font-size:24px;">
 Become the Time Series Expert for your organization.
diff --git a/_pkgdown.yml b/_pkgdown.yml
index ef0d781..6e894fc 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -77,7 +77,6 @@ reference:
     - "ensemble_average"
     - "ensemble_weighted"
     - "ensemble_model_spec"
-    - "modeltime_fit_resamples"
   - title: Nested Ensemble Forecasting
     desc: Make ensembles with the nested forecasting workflow
     contents:
diff --git a/man/ensemble_average.Rd b/man/ensemble_average.Rd
index 598b3ef..3858aa9 100644
--- a/man/ensemble_average.Rd
+++ b/man/ensemble_average.Rd
@@ -36,7 +36,7 @@ underlying models forecast at each timestamp
 library(tidymodels)
 library(modeltime)
 library(modeltime.ensemble)
-library(tidyverse)
+library(dplyr)
 library(timetk)
 
 # Make an ensemble from a Modeltime Table
diff --git a/man/ensemble_model_spec.Rd b/man/ensemble_model_spec.Rd
index 41589fd..5b1556c 100644
--- a/man/ensemble_model_spec.Rd
+++ b/man/ensemble_model_spec.Rd
@@ -103,7 +103,7 @@ up parallelization using \code{tune} via one of the backends such as
 library(tidymodels)
 library(modeltime)
 library(modeltime.ensemble)
-library(tidyverse)
+library(dplyr)
 library(timetk)
 
 # Step 1: Make resample predictions for submodels
diff --git a/man/ensemble_weighted.Rd b/man/ensemble_weighted.Rd
index c4d4348..aec1df4 100644
--- a/man/ensemble_weighted.Rd
+++ b/man/ensemble_weighted.Rd
@@ -34,7 +34,7 @@ The weighted method uses uses \code{loadings} by applying a
 library(tidymodels)
 library(modeltime)
 library(modeltime.ensemble)
-library(tidyverse)
+library(dplyr)
 library(timetk)
 
 # Make an ensemble from a Modeltime Table
diff --git a/modeltime.ensemble.Rproj b/modeltime.ensemble.Rproj
index c690b7c..09608cf 100644
--- a/modeltime.ensemble.Rproj
+++ b/modeltime.ensemble.Rproj
@@ -19,3 +19,7 @@ BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
 PackageRoxygenize: rd,collate,namespace
+
+UseNativePipeOperator: No
+
+SpellingDictionary: en_US
diff --git a/tests/testthat.R b/tests/testthat.R
index b8b7c35..bfb81a2 100644
--- a/tests/testthat.R
+++ b/tests/testthat.R
@@ -8,11 +8,9 @@ library(modeltime.resample)
 
 # Model dependencies
 library(xgboost)
-library(earth)
 library(glmnet)
 
 # Core Packages
-library(tidyverse)
 library(timetk)
 library(lubridate)
 
diff --git a/tests/testthat/test-conf_by_id.R b/tests/testthat/test-conf_by_id.R
index ccae134..b278545 100644
--- a/tests/testthat/test-conf_by_id.R
+++ b/tests/testthat/test-conf_by_id.R
@@ -8,12 +8,10 @@ test_that("Accuracy & CI by ID", {
     library(tidymodels)
     library(timetk)
     library(modeltime)
-    library(tidyverse)
 
     # Data
     data <- walmart_sales_weekly %>%
-        select(id, Date, Weekly_Sales) %>%
-        set_names(c("ID", "date", "value"))
+        dplyr::select(ID = id, date = Date, value = Weekly_Sales)
 
     splits <- data %>% time_series_split(assess = "3 months", cumulative = TRUE)
 
diff --git a/tests/testthat/test-ensemble_average.R b/tests/testthat/test-ensemble_average.R
index 100fff8..b42479f 100644
--- a/tests/testthat/test-ensemble_average.R
+++ b/tests/testthat/test-ensemble_average.R
@@ -54,7 +54,7 @@ m750_models_2 <- modeltime_table(
 # Median ----
 test_that("ensemble_average(type = 'median')", {
 
-    testthat::skip_on_cran()
+    skip_on_cran()
 
     ensemble_fit_median <- m750_models_2 %>%
         ensemble_average(type = "median")
diff --git a/tests/testthat/test-ensemble_model_spec.R b/tests/testthat/test-ensemble_model_spec.R
index ff42a0f..9f86728 100644
--- a/tests/testthat/test-ensemble_model_spec.R
+++ b/tests/testthat/test-ensemble_model_spec.R
@@ -50,7 +50,7 @@ m750_models_2 <- modeltime_table(
 # SETUP ----
 
 
-testthat::test_that("ensemble_model_spec", {
+test_that("ensemble_model_spec", {
 
     skip_on_cran()
 
diff --git a/tests/testthat/test-nested-ensembles.R b/tests/testthat/test-nested-ensembles.R
index 881b5c6..237d075 100644
--- a/tests/testthat/test-nested-ensembles.R
+++ b/tests/testthat/test-nested-ensembles.R
@@ -1,11 +1,10 @@
 context("TEST: nested ensembles")
 
 # 1- SEQUENTIAL ----
-testthat::test_that("Nested Ensembles Work - sequential", {
+test_that("Nested Ensembles Work - sequential", {
 
     data_tbl <- walmart_sales_weekly %>%
-        select(id, Date, Weekly_Sales) %>%
-        set_names(c("id", "date", "value")) %>%
+        select(id, date = Date, value = Weekly_Sales) %>%
         filter(id %in% c("1_1", "1_3"))
 
     nested_data_tbl <- data_tbl %>%
@@ -59,7 +58,7 @@ testthat::test_that("Nested Ensembles Work - sequential", {
 
 
     # EXPECT WARNING DUE TO BAD MODEL ----
-    testthat::expect_warning({
+    expect_warning({
         nested_modeltime_tbl <- modeltime_nested_fit(
             # Nested data
             nested_data = nested_data_tbl,
@@ -86,7 +85,7 @@ testthat::test_that("Nested Ensembles Work - sequential", {
     model_table <- nested_ensemble_1_tbl %>%
         extract_nested_modeltime_table()
 
-    testthat::expect_equal(
+    expect_equal(
         model_table$.model_desc[4], "ENSEMBLE (MEAN): 2 MODELS"
     )
 
@@ -94,10 +93,7 @@ testthat::test_that("Nested Ensembles Work - sequential", {
         extract_nested_test_accuracy() %>%
         filter(.model_desc == "ENSEMBLE (MEAN): 2 MODELS")
 
-    testthat::expect_is(
-        model_accuracy %>% pull(mae),
-        "numeric"
-    )
+    expect_type(model_accuracy$mae, "double")
 
     # Weighted Ensemble ----
 
@@ -115,15 +111,8 @@ testthat::test_that("Nested Ensembles Work - sequential", {
         extract_nested_modeltime_table(1) %>%
         pluck(".model", 5, "fit", "loadings_tbl")
 
-    testthat::expect_is(
-        loadings_tbl$.loadings,
-        "numeric"
-    )
-
-    testthat::expect_equal(
-        length(loadings_tbl$.loadings),
-        2
-    )
+    expect_type(loadings_tbl$.loadings, "double")
+    expect_length(loadings_tbl$.loadings, 2)
 
     # * TEST LOADINGS TOO SHORT ----
     nested_ensemble_2_tbl <- nested_ensemble_1_tbl %>%
@@ -139,15 +128,8 @@ testthat::test_that("Nested Ensembles Work - sequential", {
         extract_nested_modeltime_table(1) %>%
         pluck(".model", 5, "fit", "loadings_tbl")
 
-    testthat::expect_is(
-        loadings_tbl$.loadings,
-        "numeric"
-    )
-
-    testthat::expect_equal(
-        length(loadings_tbl$.loadings),
-        1
-    )
+    expect_type(loadings_tbl$.loadings, "double")
+    expect_length(loadings_tbl$.loadings, 1)
 
     # CHECK METRICS
 
@@ -166,7 +148,7 @@ testthat::test_that("Nested Ensembles Work - sequential", {
         mutate(check = max(c_across(model_1:model_2))) %>%
         ungroup()
 
-    testthat::expect_equivalent(metrics_rsq$model_5, metrics_rsq$check)
+    expect_equal(metrics_rsq$model_5, metrics_rsq$check, check.attributes = FALSE)
 
 
 
@@ -196,21 +178,20 @@ testthat::test_that("Nested Ensembles Work - sequential", {
         mutate(check = min(c_across(model_1:model_2))) %>%
         ungroup()
 
-    testthat::expect_equivalent(metrics_rmse$model_5, metrics_rmse$check)
+    expect_equal(metrics_rmse$model_5, metrics_rmse$check, check.attributes = FALSE)
 
 })
 
 # 2. PARALLEL ----
 
-testthat::test_that("Nested Ensembles Work - parallel", {
+test_that("Nested Ensembles Work - parallel", {
 
-    testthat::skip_on_cran()
+    skip_on_cran()
 
     parallel_start(2)
 
     data_tbl <- walmart_sales_weekly %>%
-        select(id, Date, Weekly_Sales) %>%
-        set_names(c("id", "date", "value")) %>%
+        select(id, date = Date, value = Weekly_Sales) %>%
         filter(id %in% c("1_1", "1_3"))
 
     nested_data_tbl <- data_tbl %>%
@@ -264,7 +245,7 @@ testthat::test_that("Nested Ensembles Work - parallel", {
 
 
     # EXPECT WARNING DUE TO BAD MODEL ----
-    testthat::expect_warning({
+    expect_warning({
         nested_modeltime_tbl <- modeltime_nested_fit(
             # Nested data
             nested_data = nested_data_tbl,
@@ -291,7 +272,7 @@ testthat::test_that("Nested Ensembles Work - parallel", {
     model_table <- nested_ensemble_1_tbl %>%
         extract_nested_modeltime_table()
 
-    testthat::expect_equal(
+    expect_equal(
         model_table$.model_desc[4], "ENSEMBLE (MEAN): 2 MODELS"
     )
 
@@ -299,10 +280,7 @@ testthat::test_that("Nested Ensembles Work - parallel", {
         extract_nested_test_accuracy() %>%
         filter(.model_desc == "ENSEMBLE (MEAN): 2 MODELS")
 
-    testthat::expect_is(
-        model_accuracy %>% pull(mae),
-        "numeric"
-    )
+    expect_type(model_accuracy$mae, "double")
 
     # Weighted Ensemble ----
 
@@ -320,15 +298,8 @@ testthat::test_that("Nested Ensembles Work - parallel", {
         extract_nested_modeltime_table(1) %>%
         pluck(".model", 5, "fit", "loadings_tbl")
 
-    testthat::expect_is(
-        loadings_tbl$.loadings,
-        "numeric"
-    )
-
-    testthat::expect_equal(
-        length(loadings_tbl$.loadings),
-        2
-    )
+    expect_type(loadings_tbl$.loadings, "double")
+    expect_length(loadings_tbl$.loadings, 2)
 
     # * TEST LOADINGS TOO SHORT ----
     nested_ensemble_2_tbl <- nested_ensemble_1_tbl %>%
@@ -344,15 +315,8 @@ testthat::test_that("Nested Ensembles Work - parallel", {
         extract_nested_modeltime_table(1) %>%
         pluck(".model", 5, "fit", "loadings_tbl")
 
-    testthat::expect_is(
-        loadings_tbl$.loadings,
-        "numeric"
-    )
-
-    testthat::expect_equal(
-        length(loadings_tbl$.loadings),
-        1
-    )
+    expect_type(loadings_tbl$.loadings, "double")
+    expect_length(loadings_tbl$.loadings, 1)
 
     # CHECK METRICS
 
@@ -371,7 +335,7 @@ testthat::test_that("Nested Ensembles Work - parallel", {
         mutate(check = max(c_across(model_1:model_2))) %>%
         ungroup()
 
-    testthat::expect_equivalent(metrics_rsq$model_5, metrics_rsq$check)
+    expect_equal(metrics_rsq$model_5, metrics_rsq$check, check.attributes = FALSE)
 
 
 
@@ -401,7 +365,7 @@ testthat::test_that("Nested Ensembles Work - parallel", {
         mutate(check = min(c_across(model_1:model_2))) %>%
         ungroup()
 
-    testthat::expect_equivalent(metrics_rmse$model_5, metrics_rmse$check)
+    expect_equal(metrics_rmse$model_5, metrics_rmse$check, check.attributes = FALSE)
 
     parallel_stop()
 
diff --git a/tests/testthat/test-panel-data.R b/tests/testthat/test-panel-data.R
index 23b2bf2..0c80077 100644
--- a/tests/testthat/test-panel-data.R
+++ b/tests/testthat/test-panel-data.R
@@ -59,14 +59,14 @@ test_that("ensemble_average(): Forecast Jumbled", {
         modeltime_calibrate(data_set)
 
     expect_equal(calibration_tbl$.type, c("Test"))
-    expect_true(all(c(".type", ".calibration_data") %in% names(calibration_tbl)))
+    expect_contains(names(calibration_tbl), c(".type", ".calibration_data"))
     expect_equal(nrow(data_set), calibration_tbl %>% pluck(".calibration_data", 1) %>% nrow())
 
     # Accuracy
     accuracy_tbl <- calibration_tbl %>%
         modeltime_accuracy()
 
-    expect_true(all(!is.na(accuracy_tbl$mae)))
+    expect_false(anyNA(accuracy_tbl$mae))
     expect_true(all(is.double(accuracy_tbl$mae)))
 
     expect_true(accuracy_tbl$mae < 500)
@@ -122,14 +122,14 @@ test_that("ensemble_weighted(): Forecast Jumbled", {
         modeltime_calibrate(data_set)
 
     expect_equal(calibration_tbl$.type, c("Test"))
-    expect_true(all(c(".type", ".calibration_data") %in% names(calibration_tbl)))
+    expect_contains(names(calibration_tbl), c(".type", ".calibration_data"))
     expect_equal(nrow(data_set), calibration_tbl %>% pluck(".calibration_data", 1) %>% nrow())
 
     # Accuracy
     accuracy_tbl <- calibration_tbl %>%
         modeltime_accuracy()
 
-    expect_true(all(!is.na(accuracy_tbl$mae)))
+    expect_false(anyNA(accuracy_tbl$mae))
     expect_true(all(is.double(accuracy_tbl$mae)))
     expect_true(accuracy_tbl$mae < 400)
 
@@ -195,14 +195,14 @@ test_that("ensemble_model_spec(): Forecast Jumbled", {
         modeltime_calibrate(data_set, quiet = FALSE)
 
     expect_equal(calibration_tbl$.type, c("Test"))
-    expect_true(all(c(".type", ".calibration_data") %in% names(calibration_tbl)))
+    expect_contains(names(calibration_tbl), c(".type", ".calibration_data"))
     expect_equal(nrow(data_set), calibration_tbl %>% pluck(".calibration_data", 1) %>% nrow())
 
     # Accuracy
     accuracy_tbl <- calibration_tbl %>%
         modeltime_accuracy()
 
-    expect_true(all(!is.na(accuracy_tbl$mae)))
+    expect_false(anyNA(accuracy_tbl$mae))
     expect_true(all(is.double(accuracy_tbl$mae)))
 
     # * Forecast ----
diff --git a/tests/testthat/test-recursive.R b/tests/testthat/test-recursive.R
index 7ec70f8..07257cd 100644
--- a/tests/testthat/test-recursive.R
+++ b/tests/testthat/test-recursive.R
@@ -4,7 +4,7 @@ context("TEST: RECURSIVE ENSEMBLES")
 # SINGLE / RECIPE / PARSNIP ----
 
 test_that("recursive ensemble 1 - single / recipe / parsnip", {
-
+    skip_if_not_installed("earth")
     FORECAST_HORIZON <- 24
 
     m750_extended <- m750 %>%
@@ -23,10 +23,10 @@ test_that("recursive ensemble 1 - single / recipe / parsnip", {
     m750_lagged <- recipe_lag %>% prep() %>% juice()
 
     train_data <- m750_lagged %>%
-        drop_na()
+        tidyr::drop_na()
 
     future_data <- m750_lagged %>%
-        filter(is.na(value))
+        dplyr::filter(is.na(value))
 
 
     # * Recursive Modeling ----
@@ -75,7 +75,7 @@ test_that("recursive ensemble 1 - single / recipe / parsnip", {
 
     # * Modeltime Refit ----
 
-    retrain_tbl <- m750_lagged %>% dplyr::slice(1:200) %>% drop_na()
+    retrain_tbl <- m750_lagged %>% dplyr::slice(1:200) %>% tidyr::drop_na()
     future_tbl  <- m750_lagged %>% dplyr::slice(201:224)
 
     refit_tbl <- modeltime_table(
@@ -108,7 +108,7 @@ test_that("recursive ensemble 1 - single / recipe / parsnip", {
 # PANEL / FUNCTION / PARSNIP & WORKFLOW ----
 
 test_that("recursive ensemble 2 - panel / function / parsnip + workflow", {
-
+    skip_if_not_installed("earth")
     # Jumble the data to make sure it forecasts properly
     FORECAST_HORIZON <- 24
 
@@ -132,10 +132,10 @@ test_that("recursive ensemble 2 - panel / function / parsnip + workflow", {
         lag_transformer_grouped()
 
     train_data <- m4_lags %>%
-        drop_na()
+        tidyr::drop_na()
 
     future_data <- m4_lags %>%
-        filter(is.na(value))
+        dplyr::filter(is.na(value))
 
     # * Recursive Modeling ----
 
@@ -186,7 +186,7 @@ test_that("recursive ensemble 2 - panel / function / parsnip + workflow", {
     ) %>%
         modeltime_forecast(
             new_data    = future_data,
-            actual_data = m4_lags %>% drop_na(),
+            actual_data = tidyr::drop_na(m4_lags),
             keep_data   = TRUE
         )
 
@@ -214,7 +214,7 @@ test_that("recursive ensemble 2 - panel / function / parsnip + workflow", {
     forecast_refit_tbl <- refit_tbl %>%
         modeltime_forecast(
             new_data    = future_data,
-            actual_data = m4_lags %>% drop_na(),
+            actual_data = tidyr::drop_na(m4_lags),
             keep_data   = TRUE
         )
 
diff --git a/vignettes/getting-started-with-modeltime-ensemble.Rmd b/vignettes/getting-started-with-modeltime-ensemble.Rmd
index 785e19c..9754d6d 100644
--- a/vignettes/getting-started-with-modeltime-ensemble.Rmd
+++ b/vignettes/getting-started-with-modeltime-ensemble.Rmd
@@ -42,7 +42,7 @@ Note that `modeltime.ensemble` has capabilities for more sophisticated model ens
 
 Load libraries to complete this short tutorial.
 
-```{r setup}
+```r
 # Time Series ML
 library(tidymodels)
 library(modeltime)
@@ -55,6 +55,19 @@ library(timetk)
 interactive <- FALSE
 ```
 
+```{r setup, include=FALSE,echo=FALSE}
+# Time Series ML
+library(tidymodels)
+library(modeltime)
+library(modeltime.ensemble)
+
+# Core
+library(dplyr)
+library(timetk)
+
+interactive <- FALSE
+```
+
 ## Collect the Data
 
 We'll use the `m750` dataset that comes with `modeltime.ensemble`. We can visualize the dataset. 
diff --git a/vignettes/nested-ensembles.Rmd b/vignettes/nested-ensembles.Rmd
index 69f3795..040b7c4 100644
--- a/vignettes/nested-ensembles.Rmd
+++ b/vignettes/nested-ensembles.Rmd
@@ -60,7 +60,7 @@ Load the following libraries.
 library(tidymodels)
 library(modeltime)
 library(modeltime.ensemble)
-library(tidyverse)
+library(dplyr)
 library(timetk)
 library(gt)
 ```
@@ -71,8 +71,7 @@ Read in the Walmart Sales Weekly data (comes with `timetk`).
 
 ```{r}
 data_tbl <- walmart_sales_weekly %>%
-    select(id, Date, Weekly_Sales) %>%
-    set_names(c("id", "date", "value")) %>%
+    select(id, date = Date, value = Weekly_Sales) %>%
     filter(id %in% c("1_1", "1_3"))
 
 data_tbl
@@ -217,8 +216,8 @@ tab_style_by_group <- function(object, ..., style) {
       style = style,
       locations = cells_body(
         rows = .[["_data"]] %>%
-          rowid_to_column("rowid") %>%
-          group_by(!! grp_col) %>%
+          tibble::rowid_to_column("rowid") %>%
+          group_by(!!grp_col) %>%
           filter(...) %>%
           ungroup() %>%
           pull(rowid)
diff --git a/vignettes/recursive-ensembles.Rmd b/vignettes/recursive-ensembles.Rmd
index ae0fc98..df96114 100644
--- a/vignettes/recursive-ensembles.Rmd
+++ b/vignettes/recursive-ensembles.Rmd
@@ -51,10 +51,9 @@ First, we need to load the necessary libraries:
 library(modeltime.ensemble)
 library(modeltime)
 library(tidymodels)
-library(earth)
 library(glmnet)
 library(xgboost)
-library(tidyverse)
+library(dplyr)
 library(lubridate)
 library(timetk)
 ```
@@ -91,7 +90,7 @@ We divide the data set into training dataset and future dataset:
 ```{r}
 train_data <- m750_lagged %>%
     filter(!is.na(value)) %>%
-    drop_na()
+    tidyr::drop_na()
 
 future_data <- m750_lagged %>%
     filter(is.na(value))
@@ -104,7 +103,7 @@ Next, we are going to create two models that we will then join into an ensemble.
 
 In a real scenario, we would typically do a pre-selection work where we would analyze more models and keep those with better performance to make the ensemble.
 
-```{r}
+```{r,eval=rlang::is_installed("earth")}
 model_fit_lm <- linear_reg() %>%
     set_engine("lm") %>%
     fit(value ~ ., data = train_data %>% select(-id))
@@ -204,7 +203,7 @@ We split into training data and future data.
 
 ```{r}
 train_data <- m4_lags %>%
-    drop_na()
+    tidyr::drop_na()
 
 future_data <- m4_lags %>%
     filter(is.na(value))