From 2d053c5468a3695313c5bf4f2c1d45b0b4a0b5ef Mon Sep 17 00:00:00 2001
From: topepo <mxkuhn@gmail.com>
Date: Fri, 14 Oct 2022 16:29:26 -0400
Subject: [PATCH 01/15] update pkgdown style

---
 _pkgdown.yml | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 0028876..28b49fe 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -2,9 +2,13 @@ url: https://applicable.tidymodels.org
 
 template:
   package: tidytemplate
-  params:
-    part_of: <a href="https://tidymodels.org">tidymodels</a>
-    footer: applicable is a part of the <strong>tidymodels</strong> ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.
+  bootstrap: 5
+  bslib:
+    primary: "#CA225E"
+
+  includes:
+      in_header: |
+        <script defer data-domain="applicable.tidymodels.org,all.tidymodels.org" src="https://plausible.io/js/plausible.js"></script>
 
 # https://github.com/tidyverse/tidytemplate for css
 

From 5b7faf68e6fe76c9feee584334e17e071c57849d Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Fri, 10 Mar 2023 16:03:25 -0800
Subject: [PATCH 02/15] update roxygennote

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index f6d0c07..94f29e0 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -46,6 +46,6 @@ Encoding: UTF-8
 Language: en-US
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.3
 Config/Needs/website: tidyverse/tidytemplate
 Config/testthat/edition: 3

From 7d65e4ad192f5de3701adb7b7dddb3beff14ece7 Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Fri, 10 Mar 2023 16:03:33 -0800
Subject: [PATCH 03/15] document()

---
 man/applicable-package.Rd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/man/applicable-package.Rd b/man/applicable-package.Rd
index c8f140b..2c711f2 100644
--- a/man/applicable-package.Rd
+++ b/man/applicable-package.Rd
@@ -6,9 +6,9 @@
 \alias{applicable-package}
 \title{applicable: A Compilation of Applicability Domain Methods}
 \description{
-\if{html}{\figure{logo.png}{options: align='right' alt='logo' width='120'}}
+\if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
 
-A modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See Netzeva et al (2005) <doi:10.1177/026119290503300209> for an overview of applicability domains.
+A modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See Netzeva et al (2005) \doi{10.1177/026119290503300209} for an overview of applicability domains.
 }
 \seealso{
 Useful links:

From 4421d4b94fe0c70df01cbea855b811233c16f815 Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Fri, 10 Mar 2023 16:04:27 -0800
Subject: [PATCH 04/15] rstudio -> posit

---
 DESCRIPTION               | 4 ++--
 man/applicable-package.Rd | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 94f29e0..358abcd 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -3,8 +3,8 @@ Title: A Compilation of Applicability Domain Methods
 Version: 0.0.1.1
 Authors@R: c(
     person("Marly", "Gotti", , "marlygotti@gmail.com", role = c("aut", "cre")),
-    person("Max", "Kuhn", , "max@rstudio.com", role = "aut"),
-    person("RStudio", role = "cph")
+    person("Max", "Kuhn", , "max@posit.co", role = "aut"),
+    person(given = "Posit Software, PBC", role = c("cph", "fnd"))
   )
 Description: A modeling package compiling applicability domain methods in
     R.  It combines different methods to measure the amount of
diff --git a/man/applicable-package.Rd b/man/applicable-package.Rd
index 2c711f2..168664e 100644
--- a/man/applicable-package.Rd
+++ b/man/applicable-package.Rd
@@ -24,12 +24,12 @@ Useful links:
 
 Authors:
 \itemize{
-  \item Max Kuhn \email{max@rstudio.com}
+  \item Max Kuhn \email{max@posit.co}
 }
 
 Other contributors:
 \itemize{
-  \item RStudio [copyright holder]
+  \item Posit Software, PBC [copyright holder, funder]
 }
 
 }

From d22a443ba56051ef407975d85310a627fbc7386c Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Fri, 10 Mar 2023 16:04:38 -0800
Subject: [PATCH 05/15] use_mit_license()

---
 LICENSE    | 4 ++--
 LICENSE.md | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/LICENSE b/LICENSE
index 770d208..dd20521 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,2 +1,2 @@
-YEAR: 2020
-COPYRIGHT HOLDER: RStudio PBC
+YEAR: 2023
+COPYRIGHT HOLDER: applicable authors
diff --git a/LICENSE.md b/LICENSE.md
index 2135807..72fe06d 100644
--- a/LICENSE.md
+++ b/LICENSE.md
@@ -1,6 +1,6 @@
 # MIT License
 
-Copyright (c) 2020 RStudio PBC
+Copyright (c) 2023 applicable authors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

From 72b6979c7bbc39b176b039266151f83c1a9f7770 Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Fri, 10 Mar 2023 16:05:00 -0800
Subject: [PATCH 06/15] use_tidy_logo()

---
 man/figures/logo.png | Bin 21008 -> 34005 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/man/figures/logo.png b/man/figures/logo.png
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N002ovPDHLkV1h;fiFN=0


From d2112a33925f2a28c282ffbaf24dde60dba6a24c Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Fri, 10 Mar 2023 16:05:16 -0800
Subject: [PATCH 07/15] use_tidy_coc()

---
 .../CODE_OF_CONDUCT.md                        | 50 +++++++++----------
 1 file changed, 24 insertions(+), 26 deletions(-)
 rename CODE_OF_CONDUCT.md => .github/CODE_OF_CONDUCT.md (81%)

diff --git a/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md
similarity index 81%
rename from CODE_OF_CONDUCT.md
rename to .github/CODE_OF_CONDUCT.md
index b36903f..3ac34c8 100644
--- a/CODE_OF_CONDUCT.md
+++ b/.github/CODE_OF_CONDUCT.md
@@ -6,8 +6,8 @@ We as members, contributors, and leaders pledge to make participation in our
 community a harassment-free experience for everyone, regardless of age, body
 size, visible or invisible disability, ethnicity, sex characteristics, gender
 identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, religion, or sexual identity and
-orientation.
+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.
 
 We pledge to act and interact in ways that contribute to an open, welcoming,
 diverse, inclusive, and healthy community.
@@ -21,25 +21,25 @@ community include:
 * Being respectful of differing opinions, viewpoints, and experiences
 * Giving and gracefully accepting constructive feedback
 * Accepting responsibility and apologizing to those affected by our mistakes,
-and learning from the experience
+  and learning from the experience
 * Focusing on what is best not just for us as individuals, but for the overall
-community
+  community
 
 Examples of unacceptable behavior include:
 
-* The use of sexualized language or imagery, and sexual attention or
-advances of any kind
+* The use of sexualized language or imagery, and sexual attention or advances of
+  any kind
 * Trolling, insulting or derogatory comments, and personal or political attacks
 * Public or private harassment
-* Publishing others' private information, such as a physical or email
-address, without their explicit permission
+* Publishing others' private information, such as a physical or email address,
+  without their explicit permission
 * Other conduct which could reasonably be considered inappropriate in a
-professional setting
+  professional setting
 
 ## Enforcement Responsibilities
 
-Community leaders are responsible for clarifying and enforcing our standards
-of acceptable behavior and will take appropriate and fair corrective action in
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
 response to any behavior that they deem inappropriate, threatening, offensive,
 or harmful.
 
@@ -50,17 +50,17 @@ decisions when appropriate.
 
 ## Scope
 
-This Code of Conduct applies within all community spaces, and also applies
-when an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail
-address, posting via an official social media account, or acting as an appointed
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
 representative at an online or offline event.
 
 ## Enforcement
 
 Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at [INSERT CONTACT
-METHOD]. All complaints will be reviewed and investigated promptly and fairly.
+reported to the community leaders responsible for enforcement at codeofconduct@posit.co. 
+All complaints will be reviewed and investigated promptly and fairly.
 
 All community leaders are obligated to respect the privacy and security of the
 reporter of any incident.
@@ -114,15 +114,13 @@ community.
 ## Attribution
 
 This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.0,
-available at https://www.contributor-covenant.org/version/2/0/
-code_of_conduct.html.
+version 2.1, available at
+<https://www.contributor-covenant.org/version/2/1/code_of_conduct.html>.
 
-Community Impact Guidelines were inspired by [Mozilla's code of conduct
-enforcement ladder](https://github.com/mozilla/diversity).
-
-[homepage]: https://www.contributor-covenant.org
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][https://github.com/mozilla/inclusion].
 
 For answers to common questions about this code of conduct, see the FAQ at
-https://www.contributor-covenant.org/faq. Translations are available at https://
-www.contributor-covenant.org/translations.
+<https://www.contributor-covenant.org/faq>. Translations are available at <https://www.contributor-covenant.org/translations>.
+
+[homepage]: https://www.contributor-covenant.org

From 0837988c8e787ee50961815cdeb11d5894dda9b3 Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Fri, 10 Mar 2023 16:06:00 -0800
Subject: [PATCH 08/15] use_tidy_github_actions()

---
 .github/workflows/R-CMD-check.yaml   | 13 +++++++------
 .github/workflows/pkgdown.yaml       |  4 ++--
 .github/workflows/pr-commands.yaml   |  4 ++--
 .github/workflows/test-coverage.yaml | 23 +++++++++++++++++++++--
 4 files changed, 32 insertions(+), 12 deletions(-)

diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 9e72d78..4e06c17 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -22,23 +22,24 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - {os: macOS-latest,   r: 'release'}
+          - {os: macos-latest,   r: 'release'}
 
           - {os: windows-latest, r: 'release'}
           # Use 3.6 to trigger usage of RTools35
           - {os: windows-latest, r: '3.6'}
+          # use 4.1 to check with rtools40's older compiler
+          - {os: windows-latest, r: '4.1'}
 
-          # Use older ubuntu to maximise backward compatibility
-          - {os: ubuntu-18.04,   r: 'devel', http-user-agent: 'release'}
-          - {os: ubuntu-18.04,   r: 'release'}
-          - {os: ubuntu-18.04,   r: 'oldrel-1'}
+          - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,   r: 'release'}
+          - {os: ubuntu-latest,   r: 'oldrel-1'}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       R_KEEP_PKG_SOURCE: yes
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - uses: r-lib/actions/setup-pandoc@v2
 
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index 0b26021..087f0b0 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -20,7 +20,7 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - uses: r-lib/actions/setup-pandoc@v2
 
@@ -39,7 +39,7 @@ jobs:
 
       - name: Deploy to GitHub pages 🚀
         if: github.event_name != 'pull_request'
-        uses: JamesIves/github-pages-deploy-action@4.1.4
+        uses: JamesIves/github-pages-deploy-action@v4.4.1
         with:
           clean: false
           branch: gh-pages
diff --git a/.github/workflows/pr-commands.yaml b/.github/workflows/pr-commands.yaml
index 97271eb..71f335b 100644
--- a/.github/workflows/pr-commands.yaml
+++ b/.github/workflows/pr-commands.yaml
@@ -14,7 +14,7 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - uses: r-lib/actions/pr-fetch@v2
         with:
@@ -51,7 +51,7 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - uses: r-lib/actions/pr-fetch@v2
         with:
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
index 4b65418..2c5bb50 100644
--- a/.github/workflows/test-coverage.yaml
+++ b/.github/workflows/test-coverage.yaml
@@ -15,7 +15,7 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - uses: r-lib/actions/setup-r@v2
         with:
@@ -27,5 +27,24 @@ jobs:
           needs: coverage
 
       - name: Test coverage
-        run: covr::codecov(quiet = FALSE)
+        run: |
+          covr::codecov(
+            quiet = FALSE,
+            clean = FALSE,
+            install_path = file.path(Sys.getenv("RUNNER_TEMP"), "package")
+          )
         shell: Rscript {0}
+
+      - name: Show testthat output
+        if: always()
+        run: |
+          ## --------------------------------------------------------------------
+          find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true
+        shell: bash
+
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v3
+        with:
+          name: coverage-test-failures
+          path: ${{ runner.temp }}/package

From 9349376f9a71006f137f579beaa08c9594a31678 Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Fri, 10 Mar 2023 16:06:26 -0800
Subject: [PATCH 09/15] pak pak

---
 README.Rmd |  4 ++--
 README.md  | 36 ++++++++++++++++++------------------
 2 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index d5aa8f1..709b57a 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -42,8 +42,8 @@ install.packages("applicable")
 Install the development version of applicable from [GitHub](https://github.com/) with:
 
 ``` r
-# install.packages("devtools")
-devtools::install_github("tidymodels/applicable")
+# install.packages("pak")
+pak::pak("tidymodels/applicable")
 ```
 
 ## Vignettes
diff --git a/README.md b/README.md
index 8b506d1..5fbc494 100644
--- a/README.md
+++ b/README.md
@@ -36,19 +36,19 @@ Install the development version of applicable from
 [GitHub](https://github.com/) with:
 
 ``` r
-# install.packages("devtools")
-devtools::install_github("tidymodels/applicable")
+# install.packages("pak")
+pak::pak("tidymodels/applicable")
 ```
 
 ## Vignettes
 
 To learn about how to use applicable, check out the vignettes:
 
--   `vignette("binary-data", "applicable")`: Learn different methods to
-    analyze binary data.
+- `vignette("binary-data", "applicable")`: Learn different methods to
+  analyze binary data.
 
--   `vignette("continuous-data", "applicable")`: Learn different methods
-    to analyze continuous data.
+- `vignette("continuous-data", "applicable")`: Learn different methods
+  to analyze continuous data.
 
 ## Contributing
 
@@ -56,18 +56,18 @@ This project is released with a [Contributor Code of
 Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
 By contributing to this project, you agree to abide by its terms.
 
--   For questions and discussions about tidymodels packages, modeling,
-    and machine learning, please [post on RStudio
-    Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
+- For questions and discussions about tidymodels packages, modeling, and
+  machine learning, please [post on RStudio
+  Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
 
--   If you think you have encountered a bug, please [submit an
-    issue](https://github.com/tidymodels/applicable/issues).
+- If you think you have encountered a bug, please [submit an
+  issue](https://github.com/tidymodels/applicable/issues).
 
--   Either way, learn how to create and share a
-    [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html)
-    (a minimal, reproducible example), to clearly communicate about your
-    code.
+- Either way, learn how to create and share a
+  [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html)
+  (a minimal, reproducible example), to clearly communicate about your
+  code.
 
--   Check out further details on [contributing guidelines for tidymodels
-    packages](https://www.tidymodels.org/contribute/) and [how to get
-    help](https://www.tidymodels.org/help/).
+- Check out further details on [contributing guidelines for tidymodels
+  packages](https://www.tidymodels.org/contribute/) and [how to get
+  help](https://www.tidymodels.org/help/).

From 811755991548d3f3e5cbecabc51922f074f90a9a Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Fri, 10 Mar 2023 16:08:27 -0800
Subject: [PATCH 10/15] delete docs/ folder

---
 docs/404.html                                 |  168 -
 docs/CNAME                                    |    1 -
 docs/LICENSE-text.html                        |  170 -
 docs/LICENSE.html                             |  174 -
 docs/apple-touch-icon-120x120.png             |  Bin 15417 -> 0 bytes
 docs/apple-touch-icon-60x60.png               |  Bin 5296 -> 0 bytes
 docs/apple-touch-icon-76x76.png               |  Bin 7554 -> 0 bytes
 docs/apple-touch-icon.png                     |  Bin 15417 -> 0 bytes
 docs/articles/binary-data.html                |  178 -
 .../figure-html/jac-plot-1.png                |  Bin 46027 -> 0 bytes
 .../figure-html/unnamed-chunk-6-1.png         |  373 -
 .../header-attrs-2.1/header-attrs.js          |   12 -
 docs/articles/continuous-data.html            |  278 -
 .../figure-html/autoplot-1.png                |  Bin 140584 -> 0 bytes
 .../figure-html/unnamed-chunk-5-1.png         |  Bin 99611 -> 0 bytes
 .../figure-html/unnamed-chunk-5-2.png         |  Bin 60697 -> 0 bytes
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 .../figure-html/unnamed-chunk-9-1.png         |  Bin 50721 -> 0 bytes
 .../header-attrs-2.1/header-attrs.js          |   12 -
 docs/articles/index.html                      |  169 -
 docs/authors.html                             |  175 -
 docs/bootstrap-toc.css                        |   60 -
 docs/bootstrap-toc.js                         |  159 -
 docs/docsearch.css                            |  148 -
 docs/docsearch.js                             |   85 -
 docs/favicon-16x16.png                        |  Bin 1291 -> 0 bytes
 docs/favicon-32x32.png                        |  Bin 2297 -> 0 bytes
 docs/favicon.ico                              |  Bin 15086 -> 0 bytes
 docs/index.html                               |  181 -
 docs/link.svg                                 |   12 -
 docs/logo.png                                 |  Bin 21008 -> 0 bytes
 docs/pkgdown.css                              |  367 -
 docs/pkgdown.js                               |  108 -
 docs/pkgdown.yml                              |   11 -
 docs/reference/ames_new.html                  |  194 -
 docs/reference/apd_hat_values.html            |  241 -
 docs/reference/apd_pca.html                   |  246 -
 docs/reference/apd_similarity-1.png           |  Bin 73049 -> 0 bytes
 docs/reference/apd_similarity.html            |  290 -
 docs/reference/applicable-package.html        |  188 -
 docs/reference/autoplot.apd_pca-1.png         |  Bin 103044 -> 0 bytes
 docs/reference/autoplot.apd_pca-2.png         |  Bin 65474 -> 0 bytes
 docs/reference/autoplot.apd_pca-3.png         |  Bin 73493 -> 0 bytes
 docs/reference/autoplot.apd_pca.html          |  204 -
 docs/reference/autoplot.apd_similarity.html   |  202 -
 docs/reference/binary.html                    |  256 -
 docs/reference/figures/logo.png               |  Bin 21008 -> 0 bytes
 docs/reference/index.html                     |  277 -
 docs/reference/okc_binary.html                |  252 -
 docs/reference/print.apd_hat_values.html      |  196 -
 docs/reference/print.apd_pca.html             |  200 -
 docs/reference/print.apd_similarity.html      |  200 -
 docs/reference/score.apd_hat_values.html      |  230 -
 docs/reference/score.apd_pca.html             |  233 -
 docs/reference/score.apd_similarity.html      |  229 -
 docs/reference/score.html                     |  197 -
 docs/sitemap.xml                              |   60 -
 docs/tidyverse-2.css                          |  127 -
 docs/tidyverse.css                            | 6531 -----------------
 docs/tocBullet.svg                            |   11 -
 63 files changed, 13405 deletions(-)
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 delete mode 100644 docs/CNAME
 delete mode 100644 docs/LICENSE-text.html
 delete mode 100644 docs/LICENSE.html
 delete mode 100644 docs/apple-touch-icon-120x120.png
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 delete mode 100644 docs/apple-touch-icon-76x76.png
 delete mode 100644 docs/apple-touch-icon.png
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 delete mode 100644 docs/articles/binary-data_files/figure-html/jac-plot-1.png
 delete mode 100644 docs/articles/binary-data_files/figure-html/unnamed-chunk-6-1.png
 delete mode 100644 docs/articles/binary-data_files/header-attrs-2.1/header-attrs.js
 delete mode 100644 docs/articles/continuous-data.html
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diff --git a/docs/404.html b/docs/404.html
deleted file mode 100644
index 89b69a2..0000000
--- a/docs/404.html
+++ /dev/null
@@ -1,168 +0,0 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-<title>Page not found (404) • applicable</title>
-
-<!-- favicons -->
-<link rel="icon" type="image/png" sizes="16x16" href="https://applicable.tidymodels.org/favicon-16x16.png">
-<link rel="icon" type="image/png" sizes="32x32" href="https://applicable.tidymodels.org/favicon-32x32.png">
-<link rel="apple-touch-icon" type="image/png" sizes="180x180" href="https://applicable.tidymodels.org/apple-touch-icon.png" />
-<link rel="apple-touch-icon" type="image/png" sizes="120x120" href="https://applicable.tidymodels.org/apple-touch-icon-120x120.png" />
-<link rel="apple-touch-icon" type="image/png" sizes="76x76" href="https://applicable.tidymodels.org/apple-touch-icon-76x76.png" />
-<link rel="apple-touch-icon" type="image/png" sizes="60x60" href="https://applicable.tidymodels.org/apple-touch-icon-60x60.png" />
-
-<!-- jquery -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script>
-
-<!-- Bootstrap -->
-<link href="https://applicable.tidymodels.org/tidyverse.css" rel="stylesheet">
-<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script>
-
-<!-- Font Awesome icons -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/all.min.css" integrity="sha256-nAmazAk6vS34Xqo0BSrTb+abbtFlgsFK7NKSi6o7Y78=" crossorigin="anonymous" />
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/v4-shims.min.css" integrity="sha256-6qHlizsOWFskGlwVOKuns+D1nB6ssZrHQrNj1wGplHc=" crossorigin="anonymous" />
-
-<!-- clipboard.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script>
-
-<!-- headroom.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/headroom.min.js" integrity="sha256-DJFC1kqIhelURkuza0AvYal5RxMtpzLjFhsnVIeuk+U=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
-
-<!-- pkgdown -->
-<link href="https://applicable.tidymodels.org/pkgdown.css" rel="stylesheet">
-<script src="https://applicable.tidymodels.org/pkgdown.js"></script>
-<link href="https://applicable.tidymodels.org/tidyverse-2.css" rel="stylesheet">
-
-
-
-<meta property="og:title" content="Page not found (404)" />
-<meta property="og:image" content="https://applicable.tidymodels.org/logo.png" />
-<meta name="twitter:card" content="summary" />
-
-
-
-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
-
-<!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-
-
-
-  </head>
-
-  <body data-spy="scroll" data-target="#toc">
-    <div class="container template-title-body">
-      <header>
-      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-
-      <div class="navbar-brand-container">
-        <a class="navbar-brand" href="https://applicable.tidymodels.org/index.html">applicable</a>
-        <div class="info">
-          <span class="partof">part of <a href="https://tidymodels.org">tidymodels</a></span>
-          <span class="version version-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.1</span>
-        </div>
-      </div>
-    </div>
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="index.html">
-    <span class="fas fa fas fa-home fa-lg"></span>
-     
-  </a>
-</li>
-<li>
-  <a href="reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="articles/binary-data.html">Applicability domain methods for binary data</a>
-    </li>
-    <li>
-      <a href="articles/continuous-data.html">Applicability domain methods for continuous data</a>
-    </li>
-  </ul>
-</li>
-        <li>
-  <a href="https://github.com/tidymodels/applicable/">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-      
-    </div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="contents col-md-9">
-    <div class="page-header">
-      <h1>Page not found (404)</h1>
-    </div>
-
-Content not found. Please use links in the navbar.
-
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-
-</div>
-
-
-
-      <footer>
-      <div class="tidyverse">
-  <p>applicable is a part of the <strong>tidymodels</strong> ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.</p>
-</div>
-
-<div class="author">
-  <p>
-    Developed by Marly Gotti, Max Kuhn.
-    Site built by <a href="https://pkgdown.r-lib.org">pkgdown</a>.
-  </p>
-</div>
-
-      </footer>
-   </div>
-
-  
-
-
-  </body>
-</html>
-
-
diff --git a/docs/CNAME b/docs/CNAME
deleted file mode 100644
index 944fc48..0000000
--- a/docs/CNAME
+++ /dev/null
@@ -1 +0,0 @@
-applicable.tidymodels.org
\ No newline at end of file
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
deleted file mode 100644
index cfe91ec..0000000
--- a/docs/LICENSE-text.html
+++ /dev/null
@@ -1,170 +0,0 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-<title>License • applicable</title>
-
-<!-- favicons -->
-<link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png">
-<link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png">
-<link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png" />
-<link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png" />
-<link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png" />
-<link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png" />
-
-<!-- jquery -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script>
-
-<!-- Bootstrap -->
-<link href="tidyverse.css" rel="stylesheet">
-<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script>
-
-<!-- Font Awesome icons -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/all.min.css" integrity="sha256-nAmazAk6vS34Xqo0BSrTb+abbtFlgsFK7NKSi6o7Y78=" crossorigin="anonymous" />
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/v4-shims.min.css" integrity="sha256-6qHlizsOWFskGlwVOKuns+D1nB6ssZrHQrNj1wGplHc=" crossorigin="anonymous" />
-
-<!-- clipboard.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script>
-
-<!-- headroom.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/headroom.min.js" integrity="sha256-DJFC1kqIhelURkuza0AvYal5RxMtpzLjFhsnVIeuk+U=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
-
-<!-- pkgdown -->
-<link href="pkgdown.css" rel="stylesheet">
-<script src="pkgdown.js"></script>
-<link href="tidyverse-2.css" rel="stylesheet">
-
-
-
-<meta property="og:title" content="License" />
-<meta property="og:image" content="https://applicable.tidymodels.org/logo.png" />
-<meta name="twitter:card" content="summary" />
-
-
-
-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
-
-<!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-
-
-
-  </head>
-
-  <body data-spy="scroll" data-target="#toc">
-    <div class="container template-title-body">
-      <header>
-      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
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-      <h1 data-toc-skip>Applicability domain methods for binary data</h1>
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-      <small class="dont-index">Source: <a href="https://github.com/tidymodels/applicable/blob/master/vignettes/binary-data.Rmd"><code>vignettes/binary-data.Rmd</code></a></small>
-      <div class="hidden name"><code>binary-data.Rmd</code></div>
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-<div id="introduction" class="section level2">
-<h2 class="hasAnchor">
-<a href="#introduction" class="anchor"></a>Introduction</h2>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">applicable</span>)</pre></body></html></div>
-<p>Similarity statistics can be used to compare data sets where all of the predictors are binary. One of the most common measures is the Jaccard index.</p>
-<p>For a training set of size <code>n</code>, there are <code>n</code> similarity statistics for each new sample. These can be summarized via the mean statistic or a quantile. In general, we want similarity to be low within the training set (i.e., a diverse training set) and high for new samples to be predicted.</p>
-<p>To analyze the Jaccard metric, <code>applicable</code> provides the following methods:</p>
-<ul>
-<li><p><code>apd_similarity</code>: analyzes samples in terms of similarity scores. For a training set of <em>n</em> samples, a new sample is compared to each, resulting in <em>n</em> similarity scores. These can be summarized into the median similarity.</p></li>
-<li><p><code>autoplot</code>: shows the cumulative probability versus the unique similarity values in the training set.</p></li>
-<li><p><code>score</code>: scores new samples using similarity methods. In particular, it calculates the similarity scores and if <code>add_percentile = TRUE</code>, it also estimates the percentile of the similarity scores.</p></li>
-</ul>
-</div>
-<div id="example" class="section level2">
-<h2 class="hasAnchor">
-<a href="#example" class="anchor"></a>Example</h2>
-<p>The example data is from two QSAR data sets where binary fingerprints are used as predictors.</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span>(<span class="no">qsar_binary</span>)</pre></body></html></div>
-<p>Let us construct the model:</p>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">jacc_sim</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/apd_similarity.html">apd_similarity</a></span>(<span class="no">binary_tr</span>)
-<span class="no">jacc_sim</span>
-<span class="co">#&gt; Applicability domain via similarity</span>
-<span class="co">#&gt; Reference data were 67 variables collected on 4330 data points.</span>
-<span class="co">#&gt; New data summarized using the mean.</span></pre></body></html></div>
-<p>As we can see below, this is a fairly diverse training set:</p>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">ggplot2</span>)
-
-<span class="co"># Plot the empirical cumulative distribution function for the training set</span>
-<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html">autoplot</a></span>(<span class="no">jacc_sim</span>)</pre></body></html></div>
-<p><img src="binary-data_files/figure-html/jac-plot-1.png" width="50%" style="display: block; margin: auto;"></p>
-<p>We can compare the similarity between new samples and the training set:</p>
-<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="co"># Summarize across all training set similarities</span>
-<span class="no">mean_sim</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/score.html">score</a></span>(<span class="no">jacc_sim</span>, <span class="kw">new_data</span> <span class="kw">=</span> <span class="no">binary_unk</span>)
-<span class="no">mean_sim</span>
-<span class="co">#&gt; # A tibble: 5 x 2</span>
-<span class="co">#&gt;   similarity similarity_pctl</span>
-<span class="co">#&gt;        &lt;dbl&gt;           &lt;dbl&gt;</span>
-<span class="co">#&gt; 1     0.376            49.8 </span>
-<span class="co">#&gt; 2     0.284            13.5 </span>
-<span class="co">#&gt; 3     0.218             6.46</span>
-<span class="co">#&gt; 4     0.452           100   </span>
-<span class="co">#&gt; 5     0.0971            5.59</span></pre></body></html></div>
-<p>Samples 3 and 5 are definitely extrapolations based on these predictors. In other words, the new samples are not similar to the training set and so predictions on them may not be very reliable.</p>
-</div>
-  </div>
-
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-  <p>applicable is a part of the <strong>tidymodels</strong> ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.</p>
-</div>
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-  <p>
-    Developed by Marly Gotti, Max Kuhn.
-    Site built by <a href="https://pkgdown.r-lib.org">pkgdown</a>.
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diff --git a/docs/articles/binary-data_files/header-attrs-2.1/header-attrs.js b/docs/articles/binary-data_files/header-attrs-2.1/header-attrs.js
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-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
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diff --git a/docs/articles/continuous-data.html b/docs/articles/continuous-data.html
deleted file mode 100644
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-      <h1 data-toc-skip>Applicability domain methods for continuous data</h1>
-            
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-      <small class="dont-index">Source: <a href="https://github.com/tidymodels/applicable/blob/master/vignettes/continuous-data.Rmd"><code>vignettes/continuous-data.Rmd</code></a></small>
-      <div class="hidden name"><code>continuous-data.Rmd</code></div>
-
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-    
-    
-<div id="introduction" class="section level2">
-<h2 class="hasAnchor">
-<a href="#introduction" class="anchor"></a>Introduction</h2>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">applicable</span>)</pre></body></html></div>
-<p><code>applicable</code> provides the following methods to analyze the applicability domain of your model:</p>
-<ul>
-<li>Principal component analysis</li>
-<li>Hat values statistics</li>
-</ul>
-</div>
-<div id="example" class="section level2">
-<h2 class="hasAnchor">
-<a href="#example" class="anchor"></a>Example</h2>
-<p>We will use the Ames IA housing data for our example.</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">AmesHousing</span>)
-<span class="no">ames</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/AmesHousing/man/make_ames.html">make_ames</a></span>()</pre></body></html></div>
-<p>There are 2,930 properties in the data.</p>
-<p>The Sale Price was recorded along with 81 predictors, including:</p>
-<ul>
-<li>Location (e.g. neighborhood) and lot information.</li>
-<li>House components (garage, fireplace, pool, porch, etc.).</li>
-<li>General assessments such as overall quality and condition.</li>
-<li>Number of bedrooms, baths, and so on.</li>
-</ul>
-<p>More details can be found in <a href="http://ww2.amstat.org/publications/jse/v19n3/decock.pdf">De Cock (2011, Journal of Statistics Education)</a>.</p>
-<p>The raw data are at <a href="http://bit.ly/2whgsQM"><code>http://bit.ly/2whgsQM</code></a> but we will use a processed version found in the <a href="https://github.com/topepo/AmesHousing"><code>AmesHousing</code></a> package. <code>applicable</code> also contains an update for these data for three new properties (although fewer fields were collected on these).</p>
-<p>To pre-process the training set, we will use the <em>recipes</em> package. We first tell the recipes that there is an additional value for the neighborhood in these data, then direct it to create dummy variables for all categorical predictors. In cases where there are no levels observed for a factor, we eliminate predictors with a single unique value, then estimate a transformation that will make the predictor distributions more symmetric. After these, the data are centered and scaled. These same transformations will be applied to the new data points using the statistics estimated from the training set.</p>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">recipes</span>)
-<span class="co">#&gt; Warning: package 'recipes' was built under R version 3.6.2</span>
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">dplyr</span>)
-
-<span class="no">ames_cols</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span>(<span class="no">ames_new</span>)
-
-<span class="no">training_data</span> <span class="kw">&lt;-</span>
-  <span class="no">ames</span> <span class="kw">%&gt;%</span>
-  <span class="co"># For consistency, only analyze the data on new properties</span>
-  <span class="kw pkg">dplyr</span><span class="kw ns">::</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(<span class="fu"><a href="https://dplyr.tidyverse.org/reference/reexports.html">one_of</a></span>(<span class="no">ames_cols</span>)) <span class="kw">%&gt;%</span>
-  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(
-    <span class="co"># There is a new neighborhood in ames_new</span>
-    <span class="kw">Neighborhood</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span>(<span class="no">Neighborhood</span>),
-    <span class="kw">Neighborhood</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span>(<span class="no">Neighborhood</span>, <span class="kw">levels</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/levels.html">levels</a></span>(<span class="no">ames_new</span>$<span class="no">Neighborhood</span>))
-  )
-
-
-<span class="no">training_recipe</span> <span class="kw">&lt;-</span>
-  <span class="fu"><a href="https://rdrr.io/pkg/recipes/man/recipe.html">recipe</a></span>( ~ <span class="no">.</span>, <span class="kw">data</span> <span class="kw">=</span> <span class="no">training_data</span>) <span class="kw">%&gt;%</span>
-  <span class="fu"><a href="https://rdrr.io/pkg/recipes/man/step_dummy.html">step_dummy</a></span>(<span class="fu"><a href="https://rdrr.io/pkg/recipes/man/has_role.html">all_nominal</a></span>()) <span class="kw">%&gt;%</span>
-  <span class="co"># Remove variables that have the same value for every data point.</span>
-  <span class="fu"><a href="https://rdrr.io/pkg/recipes/man/step_zv.html">step_zv</a></span>(<span class="fu"><a href="https://rdrr.io/pkg/recipes/man/has_role.html">all_predictors</a></span>()) <span class="kw">%&gt;%</span>
-  <span class="co"># Transform variables to be distributed as Gaussian-like as possible.</span>
-  <span class="fu"><a href="https://rdrr.io/pkg/recipes/man/step_YeoJohnson.html">step_YeoJohnson</a></span>(<span class="fu"><a href="https://rdrr.io/pkg/recipes/man/has_role.html">all_numeric</a></span>()) <span class="kw">%&gt;%</span>
-  <span class="co"># Normalize numeric data to have a mean of zero and</span>
-  <span class="co"># standard deviation of one.</span>
-  <span class="fu"><a href="https://rdrr.io/pkg/recipes/man/step_normalize.html">step_normalize</a></span>(<span class="fu"><a href="https://rdrr.io/pkg/recipes/man/has_role.html">all_numeric</a></span>())</pre></body></html></div>
-<div id="principal-component-analysis" class="section level3">
-<h3 class="hasAnchor">
-<a href="#principal-component-analysis" class="anchor"></a>Principal Component Analysis</h3>
-<p>The following functions in <code>applicable</code> are used for principal component analysis:</p>
-<ul>
-<li>
-<code>apd_pca</code>: computes the principal components that account for up to either 95% or the provided <code>threshold</code> of variability. It also computes the percentiles of the principal components and the mean of each principal component.</li>
-<li>
-<code>autoplot</code>: plots the distribution function for pcas. You can also provide an optional set of <code>dplyr</code> selectors, such as <code><a href="https://dplyr.tidyverse.org/reference/reexports.html">dplyr::matches()</a></code> or <code><a href="https://dplyr.tidyverse.org/reference/reexports.html">dplyr::starts_with()</a></code>, for selecting which variables should be shown in the plot.</li>
-<li>
-<code>score</code>: calculates the principal components of the new data and their percentiles as compared to the training data. The number of principal components computed depends on the <code>threshold</code> given at fit time. It also computes the multivariate distance between each principal component and its mean.</li>
-</ul>
-<p>Let us apply <code>apd_pca</code> modeling function to our data:</p>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">ames_pca</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/apd_pca.html">apd_pca</a></span>(<span class="no">training_recipe</span>, <span class="no">training_data</span>)
-<span class="no">ames_pca</span>
-<span class="co">#&gt; # Predictors:</span>
-<span class="co">#&gt;    50</span>
-<span class="co">#&gt; # Principal Components:</span>
-<span class="co">#&gt;    118 components were needed</span>
-<span class="co">#&gt;    to capture at least 95% of the</span>
-<span class="co">#&gt;    total variation in the predictors.</span></pre></body></html></div>
-<p>Since no <code>threshold</code> was provided, the function computed the number of principal components that accounted for at most 95% of the total variance.</p>
-<p>For illustration, setting <code>threshold = 0.25</code> or 25%, we now need only 10 principal components:</p>
-<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">ames_pca</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/apd_pca.html">apd_pca</a></span>(<span class="no">training_recipe</span>, <span class="no">training_data</span>, <span class="kw">threshold</span> <span class="kw">=</span> <span class="fl">0.25</span>)
-<span class="no">ames_pca</span>
-<span class="co">#&gt; # Predictors:</span>
-<span class="co">#&gt;    50</span>
-<span class="co">#&gt; # Principal Components:</span>
-<span class="co">#&gt;    10 components were needed</span>
-<span class="co">#&gt;    to capture at least 25% of the</span>
-<span class="co">#&gt;    total variation in the predictors.</span></pre></body></html></div>
-<p>Plotting the distribution function for the PCA scores is also helpful:</p>
-<div class="sourceCode" id="cb6"><html><body><pre class="r">
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">ggplot2</span>)
-<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html">autoplot</a></span>(<span class="no">ames_pca</span>)</pre></body></html></div>
-<p><img src="continuous-data_files/figure-html/autoplot-1.png" width="768" style="display: block; margin: auto;"></p>
-<p>You can use regular expressions to plot a smaller subset of the pca statistics:</p>
-<p><img src="continuous-data_files/figure-html/unnamed-chunk-5-1.png" width="768"><img src="continuous-data_files/figure-html/unnamed-chunk-5-2.png" width="768"></p>
-<p>The <code>score</code> function compares the training data to new samples. Let’s go back to the case where we capture 95% of the variation in the predictors and score the new samples. Since we used the recipe interface, we can give the score function the original data:</p>
-<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">ames_pca</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/apd_pca.html">apd_pca</a></span>(<span class="no">training_recipe</span>, <span class="no">training_data</span>)
-<span class="no">pca_score</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/score.html">score</a></span>(<span class="no">ames_pca</span>, <span class="no">ames_new</span>)
-<span class="no">pca_score</span> <span class="kw">%&gt;%</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(<span class="fu"><a href="https://dplyr.tidyverse.org/reference/reexports.html">matches</a></span>(<span class="st">"PC00[1-3]"</span>), <span class="fu"><a href="https://dplyr.tidyverse.org/reference/reexports.html">contains</a></span>(<span class="st">"distance"</span>))
-<span class="co">#&gt; # A tibble: 3 x 8</span>
-<span class="co">#&gt;   PC001 PC002  PC003 PC001_pctl PC002_pctl PC003_pctl distance distance_pctl</span>
-<span class="co">#&gt;   &lt;dbl&gt; &lt;dbl&gt;  &lt;dbl&gt;      &lt;dbl&gt;      &lt;dbl&gt;      &lt;dbl&gt;    &lt;dbl&gt;         &lt;dbl&gt;</span>
-<span class="co">#&gt; 1 -4.86 0.870 -0.457       87.0       17.7       26.1     6.33          1.85</span>
-<span class="co">#&gt; 2 -2.85 0.913  0.360       51.7       19.0       21.0     7.64         17.9 </span>
-<span class="co">#&gt; 3 -4.63 0.572 -1.21        84.8       11.7       56.5     7.58         17.1</span></pre></body></html></div>
-<p>Notice how the samples, displayed in red, are fairly dissimilar to the training set in the first component:</p>
-<p><img src="continuous-data_files/figure-html/unnamed-chunk-6-1.png" width="768"></p>
-<p>What is driving the first component? We can look at which predictors have the largest values in the rotation matrix (i.e. the values that define the linear combinations in the PC scores). The top five are:</p>
-<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="co"># `ames_pca$pcs` is the output of `prcomp()`</span>
-<span class="no">comp_one</span> <span class="kw">&lt;-</span> <span class="no">ames_pca</span>$<span class="no">pcs</span>$<span class="no">rotation</span>[, <span class="fl">1</span>]
-<span class="no">comp_one</span>[<span class="fu"><a href="https://rdrr.io/r/base/order.html">order</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/MathFun.html">abs</a></span>(<span class="no">comp_one</span>), <span class="kw">decreasing</span> <span class="kw">=</span> <span class="fl">TRUE</span>)] <span class="kw">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(<span class="fl">5</span>)
-<span class="co">#&gt;       Year_Built      Garage_Cars Foundation_PConc   Year_Remod_Add      Garage_Area </span>
-<span class="co">#&gt;       -0.2537606       -0.2176448       -0.2100323       -0.2050218       -0.2019180</span></pre></body></html></div>
-<p>These three houses are extreme in the most influential variable (year built) since they were new homes. The also tend to have fairly large garages:</p>
-<p><img src="continuous-data_files/figure-html/unnamed-chunk-8-1.png" width="768"></p>
-<p>This may be what is driving the first component.</p>
-<p>However, the overall distance values are relatively small, which indicates that, overall, these new houses are not outside the mainstream of the data.</p>
-</div>
-<div id="hat-values" class="section level3">
-<h3 class="hasAnchor">
-<a href="#hat-values" class="anchor"></a>Hat Values</h3>
-<p>The <a href="https://en.wikipedia.org/wiki/Leverage_(statistics)">Hat or leverage values</a> are based on the numerics of linear regression. The measure the distance of a data point to the center of the training set distribution. For example, if the numeric training set matrix was <span class="math inline">\(X_{n \times p}\)</span>, the <em>hat matrix</em> for the training set would be computed using</p>
-<p><span class="math display">\[H = X'(X'X)^{-1}X\]</span></p>
-<p>The corresponding hat values for the training would be the diagonals of <span class="math inline">\(H\)</span>. These values can be computed using <code><a href="https://rdrr.io/r/stats/influence.measures.html">stats::hatvalues(lm_model)</a></code> but only for an <code>lm</code> model object. Also, it cannot compute the values for new samples.</p>
-<p>Suppose that we had a new, unknown sample (as a <span class="math inline">\(p \times 1\)</span> data vector <span class="math inline">\(u\)</span>). The hat value for this sample would be</p>
-<p><span class="math display">\[h = u^\intercal(X^\intercal X)^{-1}u\]</span>.</p>
-<p>The following functions in <code>applicable</code> are used to compute the hat values of your model:</p>
-<ul>
-<li>
-<code>apd_hat_values</code>: computes the matrix <span class="math inline">\((X^\intercal X)^{-1}\)</span>.</li>
-<li>
-<code>score</code>: calculates the hat values of new samples and their percentiles.</li>
-</ul>
-<p>Two caveats for using the hat values:</p>
-<ol style="list-style-type: decimal">
-<li><p>The numerical methods are less tolerant than PCA. For example, extremely correlated predictors will degrade the ability of the hat values to be effectively used. Also, since an inverse is used, there cannot be an linear dependencies within <span class="math inline">\(X\)</span>. To resolve this the former example, the recipe step <code><a href="https://rdrr.io/pkg/recipes/man/step_corr.html">recipes::step_corr()</a></code> can be used to reduce correlation. For the latter issue, <code>recipes::step_lincomp()</code> will identify and remove linear dependencies in the data (as shown below).</p></li>
-<li><p>When using a linear or logistic model, the model adds an intercept columns of ones to <span class="math inline">\(X\)</span>. For equivalent computations, you should add a vector or ones to the data or use <code><a href="https://rdrr.io/pkg/recipes/man/step_intercept.html">recipes::step_intercept()</a></code>.</p></li>
-</ol>
-<p>Let us apply <code>apd_hat_values</code> modeling function to our data (while ensuring that there are no linear dependencies):</p>
-<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="no">non_singular_recipe</span> <span class="kw">&lt;-</span>
-  <span class="no">training_recipe</span> <span class="kw">%&gt;%</span>
-  <span class="fu"><a href="https://rdrr.io/pkg/recipes/man/step_lincomb.html">step_lincomb</a></span>(<span class="fu"><a href="https://rdrr.io/pkg/recipes/man/has_role.html">all_predictors</a></span>())
-
-<span class="co"># Recipe interface</span>
-<span class="no">ames_hat</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/apd_hat_values.html">apd_hat_values</a></span>(<span class="no">non_singular_recipe</span>, <span class="no">training_data</span>)</pre></body></html></div>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="tidyverse">
-  <p>applicable is a part of the <strong>tidymodels</strong> ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.</p>
-</div>
-
-<div class="author">
-  <p>
-    Developed by Marly Gotti, Max Kuhn.
-    Site built by <a href="https://pkgdown.r-lib.org">pkgdown</a>.
-  </p>
-</div>
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-</div>
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deleted file mode 100644
index dd57d92..0000000
--- a/docs/articles/continuous-data_files/header-attrs-2.1/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
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-});
diff --git a/docs/articles/index.html b/docs/articles/index.html
deleted file mode 100644
index dbebf94..0000000
--- a/docs/articles/index.html
+++ /dev/null
@@ -1,169 +0,0 @@
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diff --git a/docs/docsearch.js b/docs/docsearch.js
deleted file mode 100644
index b35504c..0000000
--- a/docs/docsearch.js
+++ /dev/null
@@ -1,85 +0,0 @@
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-
-  var content = hit._highlightResult.content;
-  if (content) {
-    words = words.concat(content.matchedWords);
-  }
-
-  // return unique words
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-
-function updateHitURL(hit) {
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-  var words = matchedWords(hit);
-  var url = "";
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-<meta property="og:description" content="A modeling package compiling applicability domain methods in R.
-    It combines different methods to measure the amount of extrapolation new
-    samples can have from the training set. See Netzeva et al (2005) 
-    &lt;doi:10.1177/026119290503300209&gt; for an overview of applicability domains. ">
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-          <span class="partof">part of <a href="https://tidymodels.org">tidymodels</a></span>
-          <span class="version version-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.1</span>
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-<a href="#applicable-" class="anchor"></a>applicable <img src="reference/figures/logo.png" align="right">
-</h1></div>
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-<div id="introduction" class="section level2">
-<h2 class="hasAnchor">
-<a href="#introduction" class="anchor"></a>Introduction</h2>
-<p>There are times when a model’s prediction should be taken with some skepticism. For example, if a new data point is substantially different from the training set, its predicted value may be suspect. In chemistry, it is not uncommon to create an “applicability domain” model that measures the amount of potential extrapolation new samples have from the training set. applicable contains different methods to measure how much a new data point is an extrapolation from the original data (if at all).</p>
-</div>
-<div id="installation" class="section level2">
-<h2 class="hasAnchor">
-<a href="#installation" class="anchor"></a>Installation</h2>
-<p>You can install the development version of applicable from <a href="https://github.com/">GitHub</a> with:</p>
-<div class="sourceCode" id="cb1"><pre class="r"><span class="co"># install.packages("devtools")</span>
-<span class="kw pkg">devtools</span><span class="kw ns">::</span><span class="fu"><a href="https://devtools.r-lib.org//reference/remote-reexports.html">install_github</a></span>(<span class="st">"tidymodels/applicable"</span>)</pre></div>
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-<a href="#vignettes" class="anchor"></a>Vignettes</h2>
-<p>To learn about how to use applicable, check out the vignettes:</p>
-<ul>
-<li><p><code><a href="articles/binary-data.html">vignette("binary-data", "applicable")</a></code>: Learn different methods to analyze binary data.</p></li>
-<li><p><code><a href="articles/continuous-data.html">vignette("continuous-data", "applicable")</a></code>: Learn different methods to analyze continuous data.</p></li>
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-<h2>Links</h2>
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-<li>Browse source code at <br><a href="https://github.com/tidymodels/applicable/">https://​github.com/​tidymodels/​applicable/​</a>
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-<li>Report a bug at <br><a href="https://github.com/tidymodels/applicable/issues">https://​github.com/​tidymodels/​applicable/​issues</a>
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-<li><small><a href="https://opensource.org/licenses/mit-license.php">MIT</a> + file <a href="LICENSE-text.html">LICENSE</a></small></li>
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-<h2>Developers</h2>
-<ul class="list-unstyled">
-<li>Marly Gotti <br><small class="roles"> Author, maintainer </small>  </li>
-<li>Max Kuhn <br><small class="roles"> Author </small>  </li>
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-    Developed by Marly Gotti, Max Kuhn.
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diff --git a/docs/pkgdown.css b/docs/pkgdown.css
deleted file mode 100644
index c01e592..0000000
--- a/docs/pkgdown.css
+++ /dev/null
@@ -1,367 +0,0 @@
-/* Sticky footer */
-
-/**
- * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
- * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
- *
- * .Site -> body > .container
- * .Site-content -> body > .container .row
- * .footer -> footer
- *
- * Key idea seems to be to ensure that .container and __all its parents__
- * have height set to 100%
- *
- */
-
-html, body {
-  height: 100%;
-}
-
-body {
-  position: relative;
-}
-
-body > .container {
-  display: flex;
-  height: 100%;
-  flex-direction: column;
-}
-
-body > .container .row {
-  flex: 1 0 auto;
-}
-
-footer {
-  margin-top: 45px;
-  padding: 35px 0 36px;
-  border-top: 1px solid #e5e5e5;
-  color: #666;
-  display: flex;
-  flex-shrink: 0;
-}
-footer p {
-  margin-bottom: 0;
-}
-footer div {
-  flex: 1;
-}
-footer .pkgdown {
-  text-align: right;
-}
-footer p {
-  margin-bottom: 0;
-}
-
-img.icon {
-  float: right;
-}
-
-img {
-  max-width: 100%;
-}
-
-/* Fix bug in bootstrap (only seen in firefox) */
-summary {
-  display: list-item;
-}
-
-/* Typographic tweaking ---------------------------------*/
-
-.contents .page-header {
-  margin-top: calc(-60px + 1em);
-}
-
-dd {
-  margin-left: 3em;
-}
-
-/* Section anchors ---------------------------------*/
-
-a.anchor {
-  margin-left: -30px;
-  display:inline-block;
-  width: 30px;
-  height: 30px;
-  visibility: hidden;
-
-  background-image: url(./link.svg);
-  background-repeat: no-repeat;
-  background-size: 20px 20px;
-  background-position: center center;
-}
-
-.hasAnchor:hover a.anchor {
-  visibility: visible;
-}
-
-@media (max-width: 767px) {
-  .hasAnchor:hover a.anchor {
-    visibility: hidden;
-  }
-}
-
-
-/* Fixes for fixed navbar --------------------------*/
-
-.contents h1, .contents h2, .contents h3, .contents h4 {
-  padding-top: 60px;
-  margin-top: -40px;
-}
-
-/* Navbar submenu --------------------------*/
-
-.dropdown-submenu {
-  position: relative;
-}
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-.dropdown-submenu>.dropdown-menu {
-  top: 0;
-  left: 100%;
-  margin-top: -6px;
-  margin-left: -1px;
-  border-radius: 0 6px 6px 6px;
-}
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-.dropdown-submenu:hover>.dropdown-menu {
-  display: block;
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-  content: " ";
-  float: right;
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-  height: 0;
-  border-color: transparent;
-  border-style: solid;
-  border-width: 5px 0 5px 5px;
-  border-left-color: #cccccc;
-  margin-top: 5px;
-  margin-right: -10px;
-}
-
-.dropdown-submenu:hover>a:after {
-  border-left-color: #ffffff;
-}
-
-.dropdown-submenu.pull-left {
-  float: none;
-}
-
-.dropdown-submenu.pull-left>.dropdown-menu {
-  left: -100%;
-  margin-left: 10px;
-  border-radius: 6px 0 6px 6px;
-}
-
-/* Sidebar --------------------------*/
-
-#pkgdown-sidebar {
-  margin-top: 30px;
-  position: -webkit-sticky;
-  position: sticky;
-  top: 70px;
-}
-
-#pkgdown-sidebar h2 {
-  font-size: 1.5em;
-  margin-top: 1em;
-}
-
-#pkgdown-sidebar h2:first-child {
-  margin-top: 0;
-}
-
-#pkgdown-sidebar .list-unstyled li {
-  margin-bottom: 0.5em;
-}
-
-/* bootstrap-toc tweaks ------------------------------------------------------*/
-
-/* All levels of nav */
-
-nav[data-toggle='toc'] .nav > li > a {
-  padding: 4px 20px 4px 6px;
-  font-size: 1.5rem;
-  font-weight: 400;
-  color: inherit;
-}
-
-nav[data-toggle='toc'] .nav > li > a:hover,
-nav[data-toggle='toc'] .nav > li > a:focus {
-  padding-left: 5px;
-  color: inherit;
-  border-left: 1px solid #878787;
-}
-
-nav[data-toggle='toc'] .nav > .active > a,
-nav[data-toggle='toc'] .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav > .active:focus > a {
-  padding-left: 5px;
-  font-size: 1.5rem;
-  font-weight: 400;
-  color: inherit;
-  border-left: 2px solid #878787;
-}
-
-/* Nav: second level (shown on .active) */
-
-nav[data-toggle='toc'] .nav .nav {
-  display: none; /* Hide by default, but at >768px, show it */
-  padding-bottom: 10px;
-}
-
-nav[data-toggle='toc'] .nav .nav > li > a {
-  padding-left: 16px;
-  font-size: 1.35rem;
-}
-
-nav[data-toggle='toc'] .nav .nav > li > a:hover,
-nav[data-toggle='toc'] .nav .nav > li > a:focus {
-  padding-left: 15px;
-}
-
-nav[data-toggle='toc'] .nav .nav > .active > a,
-nav[data-toggle='toc'] .nav .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav .nav > .active:focus > a {
-  padding-left: 15px;
-  font-weight: 500;
-  font-size: 1.35rem;
-}
-
-/* orcid ------------------------------------------------------------------- */
-
-.orcid {
-  font-size: 16px;
-  color: #A6CE39;
-  /* margins are required by official ORCID trademark and display guidelines */
-  margin-left:4px;
-  margin-right:4px;
-  vertical-align: middle;
-}
-
-/* Reference index & topics ----------------------------------------------- */
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-.ref-index th {font-weight: normal;}
-
-.ref-index td {vertical-align: top;}
-.ref-index .icon {width: 40px;}
-.ref-index .alias {width: 40%;}
-.ref-index-icons .alias {width: calc(40% - 40px);}
-.ref-index .title {width: 60%;}
-
-.ref-arguments th {text-align: right; padding-right: 10px;}
-.ref-arguments th, .ref-arguments td {vertical-align: top;}
-.ref-arguments .name {width: 20%;}
-.ref-arguments .desc {width: 80%;}
-
-/* Nice scrolling for wide elements --------------------------------------- */
-
-table {
-  display: block;
-  overflow: auto;
-}
-
-/* Syntax highlighting ---------------------------------------------------- */
-
-pre {
-  word-wrap: normal;
-  word-break: normal;
-  border: 1px solid #eee;
-}
-
-pre, code {
-  background-color: #f8f8f8;
-  color: #333;
-}
-
-pre code {
-  overflow: auto;
-  word-wrap: normal;
-  white-space: pre;
-}
-
-pre .img {
-  margin: 5px 0;
-}
-
-pre .img img {
-  background-color: #fff;
-  display: block;
-  height: auto;
-}
-
-code a, pre a {
-  color: #375f84;
-}
-
-a.sourceLine:hover {
-  text-decoration: none;
-}
-
-.fl      {color: #1514b5;}
-.fu      {color: #000000;} /* function */
-.ch,.st  {color: #036a07;} /* string */
-.kw      {color: #264D66;} /* keyword */
-.co      {color: #888888;} /* comment */
-
-.message { color: black;   font-weight: bolder;}
-.error   { color: orange;  font-weight: bolder;}
-.warning { color: #6A0366; font-weight: bolder;}
-
-/* Clipboard --------------------------*/
-
-.hasCopyButton {
-  position: relative;
-}
-
-.btn-copy-ex {
-  position: absolute;
-  right: 0;
-  top: 0;
-  visibility: hidden;
-}
-
-.hasCopyButton:hover button.btn-copy-ex {
-  visibility: visible;
-}
-
-/* headroom.js ------------------------ */
-
-.headroom {
-  will-change: transform;
-  transition: transform 200ms linear;
-}
-.headroom--pinned {
-  transform: translateY(0%);
-}
-.headroom--unpinned {
-  transform: translateY(-100%);
-}
-
-/* mark.js ----------------------------*/
-
-mark {
-  background-color: rgba(255, 255, 51, 0.5);
-  border-bottom: 2px solid rgba(255, 153, 51, 0.3);
-  padding: 1px;
-}
-
-/* vertical spacing after htmlwidgets */
-.html-widget {
-  margin-bottom: 10px;
-}
-
-/* fontawesome ------------------------ */
-
-.fab {
-    font-family: "Font Awesome 5 Brands" !important;
-}
-
-/* don't display links in code chunks when printing */
-/* source: https://stackoverflow.com/a/10781533 */
-@media print {
-  code a:link:after, code a:visited:after {
-    content: "";
-  }
-}
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
deleted file mode 100644
index 7e7048f..0000000
--- a/docs/pkgdown.js
+++ /dev/null
@@ -1,108 +0,0 @@
-/* http://gregfranko.com/blog/jquery-best-practices/ */
-(function($) {
-  $(function() {
-
-    $('.navbar-fixed-top').headroom();
-
-    $('body').css('padding-top', $('.navbar').height() + 10);
-    $(window).resize(function(){
-      $('body').css('padding-top', $('.navbar').height() + 10);
-    });
-
-    $('[data-toggle="tooltip"]').tooltip();
-
-    var cur_path = paths(location.pathname);
-    var links = $("#navbar ul li a");
-    var max_length = -1;
-    var pos = -1;
-    for (var i = 0; i < links.length; i++) {
-      if (links[i].getAttribute("href") === "#")
-        continue;
-      // Ignore external links
-      if (links[i].host !== location.host)
-        continue;
-
-      var nav_path = paths(links[i].pathname);
-
-      var length = prefix_length(nav_path, cur_path);
-      if (length > max_length) {
-        max_length = length;
-        pos = i;
-      }
-    }
-
-    // Add class to parent <li>, and enclosing <li> if in dropdown
-    if (pos >= 0) {
-      var menu_anchor = $(links[pos]);
-      menu_anchor.parent().addClass("active");
-      menu_anchor.closest("li.dropdown").addClass("active");
-    }
-  });
-
-  function paths(pathname) {
-    var pieces = pathname.split("/");
-    pieces.shift(); // always starts with /
-
-    var end = pieces[pieces.length - 1];
-    if (end === "index.html" || end === "")
-      pieces.pop();
-    return(pieces);
-  }
-
-  // Returns -1 if not found
-  function prefix_length(needle, haystack) {
-    if (needle.length > haystack.length)
-      return(-1);
-
-    // Special case for length-0 haystack, since for loop won't run
-    if (haystack.length === 0) {
-      return(needle.length === 0 ? 0 : -1);
-    }
-
-    for (var i = 0; i < haystack.length; i++) {
-      if (needle[i] != haystack[i])
-        return(i);
-    }
-
-    return(haystack.length);
-  }
-
-  /* Clipboard --------------------------*/
-
-  function changeTooltipMessage(element, msg) {
-    var tooltipOriginalTitle=element.getAttribute('data-original-title');
-    element.setAttribute('data-original-title', msg);
-    $(element).tooltip('show');
-    element.setAttribute('data-original-title', tooltipOriginalTitle);
-  }
-
-  if(ClipboardJS.isSupported()) {
-    $(document).ready(function() {
-      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' type = 'submit' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left auto' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
-
-      $(".examples, div.sourceCode").addClass("hasCopyButton");
-
-      // Insert copy buttons:
-      $(copyButton).prependTo(".hasCopyButton");
-
-      // Initialize tooltips:
-      $('.btn-copy-ex').tooltip({container: 'body'});
-
-      // Initialize clipboard:
-      var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', {
-        text: function(trigger) {
-          return trigger.parentNode.textContent;
-        }
-      });
-
-      clipboardBtnCopies.on('success', function(e) {
-        changeTooltipMessage(e.trigger, 'Copied!');
-        e.clearSelection();
-      });
-
-      clipboardBtnCopies.on('error', function() {
-        changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
-      });
-    });
-  }
-})(window.jQuery || window.$)
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
deleted file mode 100644
index 88de4c4..0000000
--- a/docs/pkgdown.yml
+++ /dev/null
@@ -1,11 +0,0 @@
-pandoc: 2.9.2.1
-pkgdown: 1.5.1
-pkgdown_sha: ~
-articles:
-  binary-data: binary-data.html
-  continuous-data: continuous-data.html
-last_built: 2020-05-12T18:53Z
-urls:
-  reference: https://applicable.tidymodels.org/reference
-  article: https://applicable.tidymodels.org/articles
-
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-    <h1>Recent Ames Iowa Houses</h1>
-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/data.R'><code>R/data.R</code></a></small>
-    <div class="hidden name"><code>ames_new.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>More data related to the set described by De Cock (2011) where data where
-data were recorded for 2,930 properties in Ames IA.</p>
-    </div>
-
-
-
-    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
-
-    <p>De Cock, D. (2011). "Ames, Iowa: Alternative to the Boston Housing
-Data as an End of Semester Regression Project," <em>Journal of Statistics
-Education</em>,  Volume 19, Number 3.</p>
-<p><a href='https://www.cityofames.org/government/departments-divisions-a-h/city-assessor'>https://www.cityofames.org/government/departments-divisions-a-h/city-assessor</a></p>
-<p><a href='https://ww2.amstat.org/publications/jse/v19n3/decock/DataDocumentation.txt'>https://ww2.amstat.org/publications/jse/v19n3/decock/DataDocumentation.txt</a></p>
-<p><a href='http://ww2.amstat.org/publications/jse/v19n3/decock.pdf'>http://ww2.amstat.org/publications/jse/v19n3/decock.pdf</a></p>
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    
-
-<dt>ames_new</dt><dd><p>a tibble</p></dd>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>This data sets includes three more properties added since the original
-reference. There are less fields in this data set; only those that could be
-transcribed from the assessor's office were included.</p>
-
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-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/hat_values-fit.R'><code>R/hat_values-fit.R</code></a></small>
-    <div class="hidden name"><code>apd_hat_values.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>apd_hat_values()</code> fits a model.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>apd_hat_values</span>(<span class='no'>x</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for default</span>
-<span class='fu'>apd_hat_values</span>(<span class='no'>x</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for data.frame</span>
-<span class='fu'>apd_hat_values</span>(<span class='no'>x</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for matrix</span>
-<span class='fu'>apd_hat_values</span>(<span class='no'>x</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for formula</span>
-<span class='fu'>apd_hat_values</span>(<span class='no'>formula</span>, <span class='no'>data</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for recipe</span>
-<span class='fu'>apd_hat_values</span>(<span class='no'>x</span>, <span class='no'>data</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>Depending on the context:</p><ul>
-<li><p>A <strong>data frame</strong> of predictors.</p></li>
-<li><p>A <strong>matrix</strong> of predictors.</p></li>
-<li><p>A <strong>recipe</strong> specifying a set of preprocessing steps
-created from <code><a href='https://rdrr.io/pkg/recipes/man/recipe.html'>recipes::recipe()</a></code>.</p></li>
-</ul></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not currently used, but required for extensibility.</p></td>
-    </tr>
-    <tr>
-      <th>formula</th>
-      <td><p>A formula specifying the predictor terms on the right-hand
-side. No outcome should be specified.</p></td>
-    </tr>
-    <tr>
-      <th>data</th>
-      <td><p>When a <strong>recipe</strong> or <strong>formula</strong> is used, <code>data</code> is specified as:</p><ul>
-<li><p>A <strong>data frame</strong> containing the predictors.</p></li>
-</ul></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>A <code>apd_hat_values</code> object.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='no'>predictors</span> <span class='kw'>&lt;-</span> <span class='no'>mtcars</span>[, -<span class='fl'>1</span>]
-
-<span class='co'># Data frame interface</span>
-<span class='no'>mod</span> <span class='kw'>&lt;-</span> <span class='fu'>apd_hat_values</span>(<span class='no'>predictors</span>)
-
-<span class='co'># Formula interface</span>
-<span class='no'>mod2</span> <span class='kw'>&lt;-</span> <span class='fu'>apd_hat_values</span>(<span class='no'>mpg</span> ~ <span class='no'>.</span>, <span class='no'>mtcars</span>)
-
-<span class='co'># Recipes interface</span>
-<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>recipes</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: package ‘recipes’ was built under R version 3.6.2</span></div><div class='output co'>#&gt; <span class='message'>Loading required package: dplyr</span></div><div class='output co'>#&gt; <span class='message'></span>
-#&gt; <span class='message'>Attaching package: ‘dplyr’</span></div><div class='output co'>#&gt; <span class='message'>The following objects are masked from ‘package:stats’:</span>
-#&gt; <span class='message'></span>
-#&gt; <span class='message'>    filter, lag</span></div><div class='output co'>#&gt; <span class='message'>The following objects are masked from ‘package:base’:</span>
-#&gt; <span class='message'></span>
-#&gt; <span class='message'>    intersect, setdiff, setequal, union</span></div><div class='output co'>#&gt; <span class='message'></span>
-#&gt; <span class='message'>Attaching package: ‘recipes’</span></div><div class='output co'>#&gt; <span class='message'>The following object is masked from ‘package:stats’:</span>
-#&gt; <span class='message'></span>
-#&gt; <span class='message'>    step</span></div><div class='input'><span class='no'>rec</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/recipes/man/recipe.html'>recipe</a></span>(<span class='no'>mpg</span> ~ <span class='no'>.</span>, <span class='no'>mtcars</span>)
-<span class='no'>rec</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/recipes/man/step_log.html'>step_log</a></span>(<span class='no'>rec</span>, <span class='no'>disp</span>)
-<span class='no'>mod3</span> <span class='kw'>&lt;-</span> <span class='fu'>apd_hat_values</span>(<span class='no'>rec</span>, <span class='no'>mtcars</span>)</div></pre>
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-    <h1>Fit a <code>apd_pca</code></h1>
-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/pca-fit.R'><code>R/pca-fit.R</code></a></small>
-    <div class="hidden name"><code>apd_pca.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>apd_pca()</code> fits a model.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>apd_pca</span>(<span class='no'>x</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for default</span>
-<span class='fu'>apd_pca</span>(<span class='no'>x</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for data.frame</span>
-<span class='fu'>apd_pca</span>(<span class='no'>x</span>, <span class='kw'>threshold</span> <span class='kw'>=</span> <span class='fl'>0.95</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for matrix</span>
-<span class='fu'>apd_pca</span>(<span class='no'>x</span>, <span class='kw'>threshold</span> <span class='kw'>=</span> <span class='fl'>0.95</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for formula</span>
-<span class='fu'>apd_pca</span>(<span class='no'>formula</span>, <span class='no'>data</span>, <span class='kw'>threshold</span> <span class='kw'>=</span> <span class='fl'>0.95</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for recipe</span>
-<span class='fu'>apd_pca</span>(<span class='no'>x</span>, <span class='no'>data</span>, <span class='kw'>threshold</span> <span class='kw'>=</span> <span class='fl'>0.95</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>Depending on the context:</p><ul>
-<li><p>A <strong>data frame</strong> of predictors.</p></li>
-<li><p>A <strong>matrix</strong> of predictors.</p></li>
-<li><p>A <strong>recipe</strong> specifying a set of preprocessing steps
-created from <code><a href='https://rdrr.io/pkg/recipes/man/recipe.html'>recipes::recipe()</a></code>.</p></li>
-</ul></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not currently used, but required for extensibility.</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>A number indicating the percentage of variance desired from
-the principal components. It must be a number greater than 0 and less or
-equal than 1.</p></td>
-    </tr>
-    <tr>
-      <th>formula</th>
-      <td><p>A formula specifying the predictor terms on the right-hand
-side. No outcome should be specified.</p></td>
-    </tr>
-    <tr>
-      <th>data</th>
-      <td><p>When a <strong>recipe</strong> or <strong>formula</strong> is used, <code>data</code> is specified as:</p><ul>
-<li><p>A <strong>data frame</strong> containing the predictors.</p></li>
-</ul></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>A <code>apd_pca</code> object.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The function computes the principal components that account for
-up to either 95% or the provided <code>threshold</code> of variability. It also
-computes the percentiles of the absolute value of the principal components.
-Additionally, it calculates the mean of each principal component.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='no'>predictors</span> <span class='kw'>&lt;-</span> <span class='no'>mtcars</span>[, -<span class='fl'>1</span>]
-
-<span class='co'># Data frame interface</span>
-<span class='no'>mod</span> <span class='kw'>&lt;-</span> <span class='fu'>apd_pca</span>(<span class='no'>predictors</span>)
-
-<span class='co'># Formula interface</span>
-<span class='no'>mod2</span> <span class='kw'>&lt;-</span> <span class='fu'>apd_pca</span>(<span class='no'>mpg</span> ~ <span class='no'>.</span>, <span class='no'>mtcars</span>)
-
-<span class='co'># Recipes interface</span>
-<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>recipes</span>)
-<span class='no'>rec</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/recipes/man/recipe.html'>recipe</a></span>(<span class='no'>mpg</span> ~ <span class='no'>.</span>, <span class='no'>mtcars</span>)
-<span class='no'>rec</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/recipes/man/step_log.html'>step_log</a></span>(<span class='no'>rec</span>, <span class='no'>disp</span>)
-<span class='no'>mod3</span> <span class='kw'>&lt;-</span> <span class='fu'>apd_pca</span>(<span class='no'>rec</span>, <span class='no'>mtcars</span>)</div></pre>
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diff --git a/docs/reference/apd_similarity-1.png b/docs/reference/apd_similarity-1.png
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-    <h1>Applicability domain methods using binary similarity analysis</h1>
-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/similarity.R'><code>R/similarity.R</code></a></small>
-    <div class="hidden name"><code>apd_similarity.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p><code>apd_similarity()</code> is used to analyze samples in terms of similarity scores
-for binary data. All features in the data should be binary (i.e. zero or
-one).</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>apd_similarity</span>(<span class='no'>x</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for default</span>
-<span class='fu'>apd_similarity</span>(<span class='no'>x</span>, <span class='kw'>quantile</span> <span class='kw'>=</span> <span class='fl'>NA_real_</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for data.frame</span>
-<span class='fu'>apd_similarity</span>(<span class='no'>x</span>, <span class='kw'>quantile</span> <span class='kw'>=</span> <span class='fl'>NA_real_</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for matrix</span>
-<span class='fu'>apd_similarity</span>(<span class='no'>x</span>, <span class='kw'>quantile</span> <span class='kw'>=</span> <span class='fl'>NA_real_</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for formula</span>
-<span class='fu'>apd_similarity</span>(<span class='no'>formula</span>, <span class='no'>data</span>, <span class='kw'>quantile</span> <span class='kw'>=</span> <span class='fl'>NA_real_</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for recipe</span>
-<span class='fu'>apd_similarity</span>(<span class='no'>x</span>, <span class='no'>data</span>, <span class='kw'>quantile</span> <span class='kw'>=</span> <span class='fl'>NA_real_</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>Depending on the context:</p><ul>
-<li><p>A <strong>data frame</strong> of binary predictors.</p></li>
-<li><p>A <strong>matrix</strong> of binary predictors.</p></li>
-<li><p>A <strong>recipe</strong> specifying a set of preprocessing steps
-created from <code><a href='https://rdrr.io/pkg/recipes/man/recipe.html'>recipes::recipe()</a></code>.</p></li>
-</ul></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Options to pass to <code><a href='https://rdrr.io/pkg/proxyC/man/simil.html'>proxyC::simil()</a></code>, such as <code>method</code>. If no
-options are specified, <code>method = "jaccard"</code> is used.</p></td>
-    </tr>
-    <tr>
-      <th>quantile</th>
-      <td><p>A real number between 0 and 1 or NA for how the similarity
-values for each sample versus the training set should be summarized. A value
-of <code>NA</code> specifies that the mean similarity is computed. Otherwise, the
-appropriate quantile is computed.</p></td>
-    </tr>
-    <tr>
-      <th>formula</th>
-      <td><p>A formula specifying the predictor terms on the right-hand
-side. No outcome should be specified.</p></td>
-    </tr>
-    <tr>
-      <th>data</th>
-      <td><p>When a <strong>recipe</strong> or <strong>formula</strong> is used, <code>data</code> is specified as:</p><ul>
-<li><p>A <strong>data frame</strong> containing the binary predictors. Any predictors with
-no 1's will be removed (with a warning).</p></li>
-</ul></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>A <code>apd_similarity</code> object.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The function computes measures of similarity for different samples
-points. For example, suppose samples <code>A</code> and <code>B</code> both contain <em>p</em> binary
-variables. First, a 2x2 table is constructed between <code>A</code> and <code>B</code> <em>across
-their elements</em>. The table will contain <em>p</em> entries across the four cells
-(see the example below). From this, different measures of likeness are
-computed.</p>
-<p>For a training set of <em>n</em> samples, a new sample is compared to each,
-resulting in <em>n</em> similarity scores. These can be summarized into a single
-value; the median similarity is used by default by the scoring function.</p>
-<p>For this method, the computational methods are fairly taxing for large data
-sets. The training set must be stored (albeit in a sparse matrix format) so
-object sizes may become large.</p>
-<p>By default, the computations are run in parallel using <em>all possible
-cores</em>. To change this, call the <code>setThreadOptions</code> function in the
-<code>RcppParallel</code> package.</p>
-    <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
-
-    <p>Leach, A. and Gillet V. (2007). <em>An Introduction to
-Chemoinformatics</em>. Springer, New York</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \donttest{</span>
-<span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span>(<span class='no'>qsar_binary</span>)
-
-<span class='no'>jacc_sim</span> <span class='kw'>&lt;-</span> <span class='fu'>apd_similarity</span>(<span class='no'>binary_tr</span>)
-<span class='no'>jacc_sim</span></div><div class='output co'>#&gt; Applicability domain via similarity
-#&gt; Reference data were 67 variables collected on 4330 data points.
-#&gt; New data summarized using the mean.</div><div class='input'>
-<span class='co'># plot the empirical cumulative distribution function (ECDF) for the training set:</span>
-<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>ggplot2</span>)
-<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/autoplot.html'>autoplot</a></span>(<span class='no'>jacc_sim</span>)</div><div class='img'><img src='apd_similarity-1.png' alt='' width='768' height='552' /></div><div class='input'>
-<span class='co'># Example calculations for two samples:</span>
-<span class='no'>A</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>as.matrix</a></span>(<span class='no'>binary_tr</span>[<span class='fl'>1</span>,])
-<span class='no'>B</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>as.matrix</a></span>(<span class='no'>binary_tr</span>[<span class='fl'>2</span>,])
-<span class='no'>xtab</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/table.html'>table</a></span>(<span class='no'>A</span>, <span class='no'>B</span>)
-<span class='no'>xtab</span></div><div class='output co'>#&gt;    B
-#&gt; A    0  1
-#&gt;   0 62  0
-#&gt;   1  1  4</div><div class='input'>
-<span class='co'># Jaccard statistic</span>
-<span class='no'>xtab</span>[<span class='fl'>2</span>, <span class='fl'>2</span>] / (<span class='no'>xtab</span>[<span class='fl'>1</span>, <span class='fl'>2</span>] + <span class='no'>xtab</span>[<span class='fl'>2</span>, <span class='fl'>1</span>] + <span class='no'>xtab</span>[<span class='fl'>2</span>, <span class='fl'>2</span>])</div><div class='output co'>#&gt; [1] 0.8</div><div class='input'>
-<span class='co'># Hamman statistic</span>
-( ( <span class='no'>xtab</span>[<span class='fl'>1</span>, <span class='fl'>1</span>] + <span class='no'>xtab</span>[<span class='fl'>2</span>, <span class='fl'>2</span>] ) - ( <span class='no'>xtab</span>[<span class='fl'>1</span>, <span class='fl'>2</span>] + <span class='no'>xtab</span>[<span class='fl'>2</span>, <span class='fl'>1</span>] ) ) / <span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span>(<span class='no'>xtab</span>)</div><div class='output co'>#&gt; [1] 0.9701493</div><div class='input'>
-<span class='co'># Faith statistic</span>
-( <span class='no'>xtab</span>[<span class='fl'>1</span>, <span class='fl'>1</span>] + <span class='no'>xtab</span>[<span class='fl'>2</span>, <span class='fl'>2</span>]/<span class='fl'>2</span> ) / <span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span>(<span class='no'>xtab</span>)</div><div class='output co'>#&gt; [1] 0.9552239</div><div class='input'>
-<span class='co'># Summarize across all training set similarities</span>
-<span class='no'>mean_sim</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='score.html'>score</a></span>(<span class='no'>jacc_sim</span>, <span class='kw'>new_data</span> <span class='kw'>=</span> <span class='no'>binary_unk</span>)
-<span class='no'>mean_sim</span></div><div class='output co'>#&gt; <span style='color: #555555;'># A tibble: 5 x 2</span><span>
-#&gt;   similarity similarity_pctl
-#&gt;        </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>           </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>
-#&gt; </span><span style='color: #555555;'>1</span><span>     0.376            49.8 
-#&gt; </span><span style='color: #555555;'>2</span><span>     0.284            13.5 
-#&gt; </span><span style='color: #555555;'>3</span><span>     0.218             6.46
-#&gt; </span><span style='color: #555555;'>4</span><span>     0.452           100   
-#&gt; </span><span style='color: #555555;'>5</span><span>     0.097</span><span style='text-decoration: underline;'>1</span><span>            5.59</div><div class='input'># }
-</div></span></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/applicable-package.R'><code>R/applicable-package.R</code></a></small>
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-    <div class='dont-index'><p>Useful links:</p><ul>
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diff --git a/docs/reference/autoplot.apd_pca.html b/docs/reference/autoplot.apd_pca.html
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-    <h1>Plot the distribution function for pcas</h1>
-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/plot.R'><code>R/plot.R</code></a></small>
-    <div class="hidden name"><code>autoplot.apd_pca.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Plot the distribution function for pcas</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for apd_pca</span>
-<span class='fu'>autoplot</span>(<span class='no'>object</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>object</th>
-      <td><p>An object produced by <code>apd_pca</code>.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>An optional set of <code>dplyr</code> selectors, such as <code><a href='https://dplyr.tidyverse.org/reference/reexports.html'>dplyr::matches()</a></code> or
-<code><a href='https://dplyr.tidyverse.org/reference/reexports.html'>dplyr::starts_with()</a></code> for selecting which variables should be shown in the
-plot.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>A <code>ggplot</code> object that shows the distribution function for each
-principal component.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>ggplot2</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>dplyr</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>modeldata</span>)
-<span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span>(<span class='no'>biomass</span>)
-
-<span class='no'>biomass_ad</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='apd_pca.html'>apd_pca</a></span>(<span class='no'>biomass</span>[, <span class='fl'>3</span>:<span class='fl'>8</span>])
-
-<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/autoplot.html'>autoplot</a></span>(<span class='no'>biomass_ad</span>)</div><div class='img'><img src='autoplot.apd_pca-1.png' alt='' width='768' height='552' /></div><div class='input'><span class='co'># Using selectors in `...`</span>
-<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/autoplot.html'>autoplot</a></span>(<span class='no'>biomass_ad</span>, <span class='no'>distance</span>) + <span class='fu'><a href='https://ggplot2.tidyverse.org/reference/scale_continuous.html'>scale_x_log10</a></span>()</div><div class='img'><img src='autoplot.apd_pca-2.png' alt='' width='768' height='552' /></div><div class='input'><span class='fu'><a href='https://ggplot2.tidyverse.org/reference/autoplot.html'>autoplot</a></span>(<span class='no'>biomass_ad</span>, <span class='fu'><a href='https://dplyr.tidyverse.org/reference/reexports.html'>matches</a></span>(<span class='st'>"PC[1-2]"</span>))</div><div class='img'><img src='autoplot.apd_pca-3.png' alt='' width='768' height='552' /></div><div class='input'>
-</div></pre>
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-  <p>
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diff --git a/docs/reference/autoplot.apd_similarity.html b/docs/reference/autoplot.apd_similarity.html
deleted file mode 100644
index b5068a5..0000000
--- a/docs/reference/autoplot.apd_similarity.html
+++ /dev/null
@@ -1,202 +0,0 @@
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-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/plot.R'><code>R/plot.R</code></a></small>
-    <div class="hidden name"><code>autoplot.apd_similarity.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Plot the cumulative distribution function for similarity metrics</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for apd_similarity</span>
-<span class='fu'>autoplot</span>(<span class='no'>object</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>object</th>
-      <td><p>An object produced by <code>apd_similarity</code>.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not currently used.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>A <code>ggplot</code> object that shows the cumulative probability versus the
-unique similarity values in the training set. Not that for large samples,
-this is an approximation based on a random sample of 5,000 training set
-points.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/Random.html'>set.seed</a></span>(<span class='fl'>535</span>)
-<span class='no'>tr_x</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/sample.html'>sample</a></span>(<span class='fl'>0</span>:<span class='fl'>1</span>, <span class='kw'>size</span> <span class='kw'>=</span> <span class='fl'>20</span> * <span class='fl'>50</span>, <span class='kw'>prob</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span>(<span class='fl'>.5</span>, <span class='fl'>2</span>),
- <span class='kw'>replace</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>20</span>)
-<span class='no'>model</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='apd_similarity.html'>apd_similarity</a></span>(<span class='no'>tr_x</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.</span>
-#&gt; <span class='warning'>Using compatibility `.name_repair`.</span>
-#&gt; <span class='warning'><span style='color: #555555;'>This warning is displayed once every 8 hours.</span><span></span>
-#&gt; <span class='warning'></span><span style='color: #555555;'>Call `lifecycle::last_warnings()` to see where this warning was generated.</span><span></span></div><div class='input'>
-</div></span></pre>
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-    <h1>Binary QSAR Data</h1>
-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/data.R'><code>R/data.R</code></a></small>
-    <div class="hidden name"><code>binary.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Binary QSAR Data</p>
-    </div>
-
-
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    
-
-<dt>binary_tr,binary_ukn</dt><dd><p>data frame frames with 67 columns</p></dd>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>These data are from two different sources on quantitative
-structure-activity relationship (QSAR) modeling and contain 67 predictors
-that are either 0 or 1. The training set contains 4,330 samples and there
-are five unknown samples (both from the <code>Mutagen</code> data in the <code>QSARdata</code>
-package).</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span>(<span class='no'>qsar_binary</span>)
-<span class='fu'><a href='https://rdrr.io/r/utils/str.html'>str</a></span>(<span class='no'>binary_tr</span>)</div><div class='output co'>#&gt; 'data.frame':	4330 obs. of  67 variables:
-#&gt;  $ predictor01: num  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor02: num  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor03: num  1 1 1 1 1 1 1 1 1 1 ...
-#&gt;  $ predictor04: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor05: num  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor06: num  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor07: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor08: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor09: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor10: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor11: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor12: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor13: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor14: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor15: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor16: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor17: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor18: int  0 0 0 1 0 0 0 0 0 0 ...
-#&gt;  $ predictor19: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor20: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor21: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor22: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor23: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor24: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor25: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor26: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor27: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor28: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor29: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor30: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor31: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor32: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor33: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor34: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor35: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor36: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor37: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor38: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor39: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor40: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor41: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor42: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor43: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor44: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor45: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor46: num  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor47: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor48: num  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor49: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor50: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor51: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor52: int  1 1 1 0 1 0 1 1 0 0 ...
-#&gt;  $ predictor53: int  0 0 1 0 1 0 0 0 0 0 ...
-#&gt;  $ predictor54: int  0 0 1 0 1 0 1 1 0 0 ...
-#&gt;  $ predictor55: int  0 0 1 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor56: int  0 0 1 0 1 0 1 0 0 0 ...
-#&gt;  $ predictor57: int  0 0 1 0 1 0 0 0 0 0 ...
-#&gt;  $ predictor58: int  0 0 1 0 1 0 1 0 0 0 ...
-#&gt;  $ predictor59: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor60: int  0 0 1 0 1 0 1 0 0 0 ...
-#&gt;  $ predictor61: int  0 0 1 0 1 0 0 0 0 0 ...
-#&gt;  $ predictor62: int  1 0 1 0 1 0 0 1 0 0 ...
-#&gt;  $ predictor63: int  0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ predictor64: int  1 1 1 0 1 0 1 1 0 0 ...
-#&gt;  $ predictor65: int  0 0 1 0 1 0 0 0 0 0 ...
-#&gt;  $ predictor66: int  1 1 1 0 1 0 1 1 0 0 ...
-#&gt;  $ predictor67: int  0 0 1 0 1 0 0 1 0 0 ...</div></pre>
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-</div>
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-<div class="author">
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-    Developed by Marly Gotti, Max Kuhn.
-    Site built by <a href="https://pkgdown.r-lib.org">pkgdown</a>.
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-      <h1>Reference</h1>
-    </div>
-
-    <table class="ref-index">
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-    <colgroup>
-      
-      <col class="alias" />
-      <col class="title" />
-    </colgroup>
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-    <tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-all-functions" class="hasAnchor"><a href="#section-all-functions" class="anchor"></a>All functions</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
-      
-      
-    </tbody><tbody>
-      
-      
-      <tr>
-        
-        <td>
-          <p><code><a href="ames_new.html">ames_new</a></code> </p>
-        </td>
-        <td><p>Recent Ames Iowa Houses</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="apd_hat_values.html">apd_hat_values()</a></code> </p>
-        </td>
-        <td><p>Fit a <code>apd_hat_values</code></p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="apd_pca.html">apd_pca()</a></code> </p>
-        </td>
-        <td><p>Fit a <code>apd_pca</code></p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="apd_similarity.html">apd_similarity()</a></code> </p>
-        </td>
-        <td><p>Applicability domain methods using binary similarity analysis</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="autoplot.apd_pca.html">autoplot(<i>&lt;apd_pca&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Plot the distribution function for pcas</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="autoplot.apd_similarity.html">autoplot(<i>&lt;apd_similarity&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Plot the cumulative distribution function for similarity metrics</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="binary.html">binary</a></code> </p>
-        </td>
-        <td><p>Binary QSAR Data</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="okc_binary.html">okc_binary</a></code> </p>
-        </td>
-        <td><p>OkCupid Binary Predictors</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="print.apd_hat_values.html">print(<i>&lt;apd_hat_values&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Print number of predictors and principal components used.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="print.apd_pca.html">print(<i>&lt;apd_pca&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Print number of predictors and principal components used.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="print.apd_similarity.html">print(<i>&lt;apd_similarity&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Print number of predictors and principal components used.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="score.html">score()</a></code> </p>
-        </td>
-        <td><p>A scoring function</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="score.apd_hat_values.html">score(<i>&lt;apd_hat_values&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Score new samples using hat values</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="score.apd_pca.html">score(<i>&lt;apd_pca&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Predict from a <code>apd_pca</code></p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="score.apd_similarity.html">score(<i>&lt;apd_similarity&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Score new samples using similarity methods</p></td>
-      </tr>
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-    <h1>OkCupid Binary Predictors</h1>
-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/data.R'><code>R/data.R</code></a></small>
-    <div class="hidden name"><code>okc_binary.Rd</code></div>
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-    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
-
-    <p>Kim (2015), "OkCupid Data for Introductory Statistics and Data Science Courses", <em>Journal of Statistics Education</em>, Volume 23, Number 2. <a href='http://www.amstat.org/publications/jse/contents_2015.html'>http://www.amstat.org/publications/jse/contents_2015.html</a></p>
-<p>Kuhn and Johnson (2020), <em>Feature Engineering and Selection</em>, Chapman and Hall/CRC . <a href='https://bookdown.org/max/FES/'>https://bookdown.org/max/FES/</a> and <a href='https://github.com/topepo/FES'>https://github.com/topepo/FES</a></p>
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    
-
-<dt>okc_binary_train,okc_binary_test</dt><dd><p>data frame frames with 61 columns</p></dd>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>Data originally from Kim (2015) includes a training and test set
-consistent with Kuhn and Johnson (2020). Predictors include ethnicity
-indicators and a set of keywords derived from text essay data.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span>(<span class='no'>okc_binary</span>)
-<span class='fu'><a href='https://rdrr.io/r/utils/str.html'>str</a></span>(<span class='no'>okc_binary_train</span>)</div><div class='output co'>#&gt; tibble [38,809 × 61] (S3: tbl_df/tbl/data.frame)
-#&gt;  $ software        : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ engineer        : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ startup         : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ tech            : num [1:38809] 0 0 0 0 1 0 0 0 0 1 ...
-#&gt;  $ computers       : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ engineering     : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ computer        : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ internet        : num [1:38809] 0 0 0 0 1 0 0 0 0 0 ...
-#&gt;  $ technology      : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ science         : num [1:38809] 0 0 0 0 1 0 0 0 0 0 ...
-#&gt;  $ programming     : num [1:38809] 0 0 0 0 0 0 0 0 0 1 ...
-#&gt;  $ technical       : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ web             : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ developer       : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ im              : num [1:38809] 1 0 1 0 1 1 0 1 1 0 ...
-#&gt;  $ programmer      : num [1:38809] 0 0 0 0 0 0 0 0 0 1 ...
-#&gt;  $ scientist       : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ code            : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ stephenson      : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ geek            : num [1:38809] 0 0 0 0 0 0 0 0 0 1 ...
-#&gt;  $ nerd            : num [1:38809] 0 0 0 0 0 0 0 0 0 1 ...
-#&gt;  $ lol             : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ biotech         : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ matrix          : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ coding          : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ geeky           : num [1:38809] 0 0 0 0 0 0 0 0 0 1 ...
-#&gt;  $ solving         : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ problems        : num [1:38809] 0 0 1 0 1 0 0 0 0 0 ...
-#&gt;  $ data            : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ fixing          : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ teacher         : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ student         : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ silicon         : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ law             : num [1:38809] 0 0 0 0 0 0 0 1 0 0 ...
-#&gt;  $ mechanical      : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ electronic      : num [1:38809] 0 0 0 0 0 0 0 0 0 1 ...
-#&gt;  $ pratchett       : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ wikipedia       : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ neal            : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ mobile          : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ math            : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ lab             : num [1:38809] 0 0 0 0 0 0 0 0 0 1 ...
-#&gt;  $ systems         : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ electronics     : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ futurama        : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ alot            : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ solve           : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ websites        : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ firefly         : num [1:38809] 0 0 0 0 0 0 0 0 0 1 ...
-#&gt;  $ valley          : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ apps            : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ lawyer          : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ asian           : num [1:38809] 1 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ black           : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ hispanic_latin  : num [1:38809] 0 0 0 0 0 0 0 1 0 0 ...
-#&gt;  $ indian          : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ middle_eastern  : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ native_american : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ other           : num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ pacific_islander: num [1:38809] 0 0 0 0 0 0 0 0 0 0 ...
-#&gt;  $ white           : num [1:38809] 1 1 1 1 1 1 1 1 1 1 ...</div></pre>
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-
-    <pre class="usage"><span class='co'># S3 method for apd_hat_values</span>
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>x</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>A <code>apd_hat_values</code> object.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not currently used, but required for extensibility.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>None</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>
-<span class='no'>model</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='apd_hat_values.html'>apd_hat_values</a></span>(~ <span class='no'>Sepal.Length</span> + <span class='no'>Sepal.Width</span>, <span class='no'>iris</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>model</span>)</div><div class='output co'>#&gt; # Predictors:
-#&gt; 2</div><div class='input'>
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/print.R'><code>R/print.R</code></a></small>
-    <div class="hidden name"><code>print.apd_pca.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Print number of predictors and principal components used.</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for apd_pca</span>
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>x</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>A <code>apd_pca</code> object.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not currently used, but required for extensibility.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>None</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>
-<span class='no'>model</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='apd_pca.html'>apd_pca</a></span>(~ <span class='no'>Sepal.Length</span> + <span class='no'>Sepal.Width</span>, <span class='no'>iris</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>model</span>)</div><div class='output co'>#&gt; # Predictors:
-#&gt;    2
-#&gt; # Principal Components:
-#&gt;    2 components were needed
-#&gt;    to capture at least 95% of the
-#&gt;    total variation in the predictors.</div><div class='input'>
-</div></pre>
-  </div>
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diff --git a/docs/reference/print.apd_similarity.html b/docs/reference/print.apd_similarity.html
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--- a/docs/reference/print.apd_similarity.html
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-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/print.R'><code>R/print.R</code></a></small>
-    <div class="hidden name"><code>print.apd_similarity.Rd</code></div>
-    </div>
-
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-    <p>Print number of predictors and principal components used.</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for apd_similarity</span>
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>x</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>A <code>apd_similarity</code> object.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not currently used, but required for extensibility.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>None</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>
-<span class='fu'><a href='https://rdrr.io/r/base/Random.html'>set.seed</a></span>(<span class='fl'>535</span>)
-<span class='no'>tr_x</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/sample.html'>sample</a></span>(<span class='fl'>0</span>:<span class='fl'>1</span>, <span class='kw'>size</span> <span class='kw'>=</span> <span class='fl'>20</span> * <span class='fl'>50</span>, <span class='kw'>prob</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span>(<span class='fl'>.5</span>, <span class='fl'>2</span>),
- <span class='kw'>replace</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), <span class='kw'>ncol</span> <span class='kw'>=</span> <span class='fl'>20</span>)
-<span class='no'>model</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='apd_similarity.html'>apd_similarity</a></span>(<span class='no'>tr_x</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>model</span>)</div><div class='output co'>#&gt; Applicability domain via similarity
-#&gt; Reference data were 20 variables collected on 50 data points.
-#&gt; New data summarized using the mean.</div><div class='input'>
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    Developed by Marly Gotti, Max Kuhn.
-    Site built by <a href="https://pkgdown.r-lib.org">pkgdown</a>.
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diff --git a/docs/reference/score.apd_hat_values.html b/docs/reference/score.apd_hat_values.html
deleted file mode 100644
index e96da95..0000000
--- a/docs/reference/score.apd_hat_values.html
+++ /dev/null
@@ -1,230 +0,0 @@
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-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/hat_values-score.R'><code>R/hat_values-score.R</code></a></small>
-    <div class="hidden name"><code>score.apd_hat_values.Rd</code></div>
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-
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-    <p>Score new samples using hat values</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for apd_hat_values</span>
-<span class='fu'><a href='score.html'>score</a></span>(<span class='no'>object</span>, <span class='no'>new_data</span>, <span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"numeric"</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>object</th>
-      <td><p>A <code>apd_hat_values</code> object.</p></td>
-    </tr>
-    <tr>
-      <th>new_data</th>
-      <td><p>A data frame or matrix of new predictors.</p></td>
-    </tr>
-    <tr>
-      <th>type</th>
-      <td><p>A single character. The type of predictions to generate.
-Valid options are:</p><ul>
-<li><p><code>"numeric"</code> for a numeric value that summarizes the hat values for
-each sample across the training set.</p></li>
-</ul></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not used, but required for extensibility.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>A tibble of predictions. The number of rows in the tibble is guaranteed
-to be the same as the number of rows in <code>new_data</code>. For <code>type = "numeric"</code>,
-the tibble contains two columns <code>hat_values</code> and <code>hat_values_pctls</code>. The
-column <code>hat_values_pctls</code> is in percent units so that a value of 11.5
-indicates that, in the training set, 11.5 percent of the training set
-samples had smaller values than the sample being scored.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='no'>train_data</span> <span class='kw'>&lt;-</span> <span class='no'>mtcars</span>[<span class='fl'>1</span>:<span class='fl'>20</span>,]
-<span class='no'>test_data</span> <span class='kw'>&lt;-</span> <span class='no'>mtcars</span>[<span class='fl'>21</span>:<span class='fl'>32</span>,]
-
-<span class='no'>hat_values_model</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='apd_hat_values.html'>apd_hat_values</a></span>(<span class='no'>train_data</span>)
-
-<span class='no'>hat_values_scoring</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='score.html'>score</a></span>(<span class='no'>hat_values_model</span>, <span class='kw'>new_data</span> <span class='kw'>=</span> <span class='no'>test_data</span>)
-<span class='no'>hat_values_scoring</span></div><div class='output co'>#&gt; <span style='color: #555555;'># A tibble: 12 x 2</span><span>
-#&gt;    hat_values hat_values_pctls
-#&gt;         </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>            </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>
-#&gt; </span><span style='color: #555555;'> 1</span><span>      1.45              1   
-#&gt; </span><span style='color: #555555;'> 2</span><span>      0.852            90.0 
-#&gt; </span><span style='color: #555555;'> 3</span><span>      1.13              1   
-#&gt; </span><span style='color: #555555;'> 4</span><span>      1.19              1   
-#&gt; </span><span style='color: #555555;'> 5</span><span>      0.901            93.2 
-#&gt; </span><span style='color: #555555;'> 6</span><span>      0.335             6.34
-#&gt; </span><span style='color: #555555;'> 7</span><span>      5.41              1   
-#&gt; </span><span style='color: #555555;'> 8</span><span>      5.91              1   
-#&gt; </span><span style='color: #555555;'> 9</span><span>      8.19              1   
-#&gt; </span><span style='color: #555555;'>10</span><span>      5.11              1   
-#&gt; </span><span style='color: #555555;'>11</span><span>     12.4               1   
-#&gt; </span><span style='color: #555555;'>12</span><span>      0.960             1   </div><div class='input'>
-</div></span></pre>
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diff --git a/docs/reference/score.apd_pca.html b/docs/reference/score.apd_pca.html
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-    <h1>Predict from a <code>apd_pca</code></h1>
-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/pca-score.R'><code>R/pca-score.R</code></a></small>
-    <div class="hidden name"><code>score.apd_pca.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Predict from a <code>apd_pca</code></p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for apd_pca</span>
-<span class='fu'><a href='score.html'>score</a></span>(<span class='no'>object</span>, <span class='no'>new_data</span>, <span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"numeric"</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>object</th>
-      <td><p>A <code>apd_pca</code> object.</p></td>
-    </tr>
-    <tr>
-      <th>new_data</th>
-      <td><p>A data frame or matrix of new samples.</p></td>
-    </tr>
-    <tr>
-      <th>type</th>
-      <td><p>A single character. The type of predictions to generate.
-Valid options are:</p><ul>
-<li><p><code>"numeric"</code> for numeric predictions.</p></li>
-</ul></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not used, but required for extensibility.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>A tibble of predictions. The number of rows in the tibble is guaranteed
-to be the same as the number of rows in <code>new_data</code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The function computes the principal components of the new data and
-their percentiles as compared to the training data. The number of principal
-components computed depends on the <code>threshold</code> given at fit time. It also
-computes the multivariate distance between each principal component and its
-mean.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='no'>train</span> <span class='kw'>&lt;-</span> <span class='no'>mtcars</span>[<span class='fl'>1</span>:<span class='fl'>20</span>,]
-<span class='no'>test</span> <span class='kw'>&lt;-</span> <span class='no'>mtcars</span>[<span class='fl'>21</span>:<span class='fl'>32</span>, -<span class='fl'>1</span>]
-
-<span class='co'># Fit</span>
-<span class='no'>mod</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='apd_pca.html'>apd_pca</a></span>(<span class='no'>mpg</span> ~ <span class='no'>cyl</span> + <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>(<span class='no'>drat</span>), <span class='no'>train</span>)
-
-<span class='co'># Predict, with preprocessing</span>
-<span class='fu'><a href='score.html'>score</a></span>(<span class='no'>mod</span>, <span class='no'>test</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: collapsing to unique 'x' values</span></div><div class='output co'>#&gt; <span class='warning'>Warning: collapsing to unique 'x' values</span></div><div class='output co'>#&gt; <span class='warning'>Warning: collapsing to unique 'x' values</span></div><div class='output co'>#&gt; <span style='color: #555555;'># A tibble: 12 x 6</span><span>
-#&gt;       PC1     PC2 distance PC1_pctl PC2_pctl distance_pctl
-#&gt;     </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>   </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>    </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>    </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>    </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>         </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>
-#&gt; </span><span style='color: #555555;'> 1</span><span> -</span><span style='color: #BB0000;'>1.16</span><span>   0.664     1.34      42.9    87.6           43.0
-#&gt; </span><span style='color: #555555;'> 2</span><span>  1.84   0.345     1.87      95.3    42.2           95.4
-#&gt; </span><span style='color: #555555;'> 3</span><span>  1.23  -</span><span style='color: #BB0000;'>0.259</span><span>     1.26      47.3    36.7           36.8
-#&gt; </span><span style='color: #555555;'> 4</span><span>  0.461 -</span><span style='color: #BB0000;'>1.03</span><span>      1.13       0      98.5           25.5
-#&gt; </span><span style='color: #555555;'> 5</span><span>  1.34  -</span><span style='color: #BB0000;'>0.157</span><span>     1.35      52.5    27.4           44.7
-#&gt; </span><span style='color: #555555;'> 6</span><span> -</span><span style='color: #BB0000;'>1.61</span><span>   0.217     1.62      89.2    31.7           89.3
-#&gt; </span><span style='color: #555555;'> 7</span><span> -</span><span style='color: #BB0000;'>1.98</span><span>  -</span><span style='color: #BB0000;'>0.159</span><span>     1.99      96.4    27.5           96.2
-#&gt; </span><span style='color: #555555;'> 8</span><span> -</span><span style='color: #BB0000;'>1.25</span><span>   0.579     1.37      48.0    82.0           59.1
-#&gt; </span><span style='color: #555555;'> 9</span><span> -</span><span style='color: #BB0000;'>0.103</span><span> -</span><span style='color: #BB0000;'>1.60</span><span>      1.60       0       1             87.3
-#&gt; </span><span style='color: #555555;'>10</span><span> -</span><span style='color: #BB0000;'>0.231</span><span> -</span><span style='color: #BB0000;'>0.065</span><span style='color: #BB0000;text-decoration: underline;'>5</span><span>    0.241      0       6.76           0  
-#&gt; </span><span style='color: #555555;'>11</span><span>  0.700 -</span><span style='color: #BB0000;'>0.793</span><span>     1.06      22.4    96.0           24.4
-#&gt; </span><span style='color: #555555;'>12</span><span> -</span><span style='color: #BB0000;'>1.64</span><span>   0.184     1.65      90.6    29.2           90.6</div><div class='input'>
-</div></span></pre>
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-    <small class="dont-index">Source: <a href='https://github.com/tidymodels/applicable/blob/master/R/similarity.R'><code>R/similarity.R</code></a></small>
-    <div class="hidden name"><code>score.apd_similarity.Rd</code></div>
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-
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-    <p>Score new samples using similarity methods</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for apd_similarity</span>
-<span class='fu'><a href='score.html'>score</a></span>(<span class='no'>object</span>, <span class='no'>new_data</span>, <span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"numeric"</span>, <span class='kw'>add_percentile</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>object</th>
-      <td><p>A <code>apd_similarity</code> object.</p></td>
-    </tr>
-    <tr>
-      <th>new_data</th>
-      <td><p>A data frame or matrix of new predictors.</p></td>
-    </tr>
-    <tr>
-      <th>type</th>
-      <td><p>A single character. The type of predictions to generate.
-Valid options are:</p><ul>
-<li><p><code>"numeric"</code> for a numeric value that summarizes the similarity values for
-each sample across the training set.</p></li>
-</ul></td>
-    </tr>
-    <tr>
-      <th>add_percentile</th>
-      <td><p>A single logical; should the percentile of the
-similarity score <em>relative to the training set values</em> by computed?</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not used, but required for extensibility.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>A tibble of predictions. The number of rows in the tibble is guaranteed
-to be the same as the number of rows in <code>new_data</code>. For <code>type = "numeric"</code>,
-the tibble contains a column called "similarity". If <code>add_percentile = TRUE</code>,
-an additional column called <code>similarity_pctl</code> will be added. These values are
-in percent units so that a value of 11.5 indicates that, in the training set,
-11.5 percent of the training set samples had smaller values than the sample
-being scored.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='co'># \donttest{</span>
-<span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span>(<span class='no'>qsar_binary</span>)
-
-<span class='no'>jacc_sim</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='apd_similarity.html'>apd_similarity</a></span>(<span class='no'>binary_tr</span>)
-
-<span class='no'>mean_sim</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='score.html'>score</a></span>(<span class='no'>jacc_sim</span>, <span class='kw'>new_data</span> <span class='kw'>=</span> <span class='no'>binary_unk</span>)
-<span class='no'>mean_sim</span></div><div class='output co'>#&gt; <span style='color: #555555;'># A tibble: 5 x 2</span><span>
-#&gt;   similarity similarity_pctl
-#&gt;        </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>           </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>
-#&gt; </span><span style='color: #555555;'>1</span><span>     0.376            49.8 
-#&gt; </span><span style='color: #555555;'>2</span><span>     0.284            13.5 
-#&gt; </span><span style='color: #555555;'>3</span><span>     0.218             6.46
-#&gt; </span><span style='color: #555555;'>4</span><span>     0.452           100   
-#&gt; </span><span style='color: #555555;'>5</span><span>     0.097</span><span style='text-decoration: underline;'>1</span><span>            5.59</div><div class='input'># }
-</div></span></pre>
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diff --git a/docs/reference/score.html b/docs/reference/score.html
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-    <pre class="usage"><span class='fu'>score</span>(<span class='no'>object</span>, <span class='no'>...</span>)
-
-<span class='co'># S3 method for default</span>
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-    font-weight: bold; }
-  .alert .close {
-    color: #fff; }
-
-.badge {
-  padding: 4px 6px 4px; }
-
-.progress {
-  position: relative;
-  z-index: 1;
-  height: 6px;
-  border-radius: 0;
-  -webkit-box-shadow: none;
-  box-shadow: none; }
-  .progress-bar {
-    -webkit-box-shadow: none;
-    box-shadow: none; }
-    .progress-bar:last-child {
-      border-radius: 0 3px 3px 0; }
-    .progress-bar:last-child:before {
-      display: block;
-      content: "";
-      position: absolute;
-      width: 100%;
-      height: 100%;
-      left: 0;
-      right: 0;
-      z-index: -1;
-      background-color: #edf4fb; }
-    .progress-bar-success:last-child.progress-bar:before {
-      background-color: #c7e7c8; }
-    .progress-bar-info:last-child.progress-bar:before {
-      background-color: #edc9f3; }
-    .progress-bar-warning:last-child.progress-bar:before {
-      background-color: #ffe0b3; }
-    .progress-bar-danger:last-child.progress-bar:before {
-      background-color: #f28e92; }
-
-.close {
-  font-size: 34px;
-  font-weight: 300;
-  line-height: 24px;
-  opacity: 0.6;
-  -webkit-transition: all 0.2s;
-  -o-transition: all 0.2s;
-  transition: all 0.2s; }
-  .close:hover {
-    opacity: 1; }
-
-.list-group-item {
-  padding: 15px; }
-
-.list-group-item-text {
-  color: #bbb; }
-
-.well {
-  border-radius: 0;
-  -webkit-box-shadow: none;
-  box-shadow: none; }
-
-.panel {
-  border: none;
-  border-radius: 2px;
-  -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3);
-  box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3); }
-  .panel-heading {
-    border-bottom: none; }
-  .panel-footer {
-    border-top: none; }
-
-.popover {
-  border: none;
-  -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3);
-  box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3); }
-
-.carousel-caption h1, .carousel-caption h2, .carousel-caption h3, .carousel-caption h4, .carousel-caption h5, .carousel-caption h6 {
-  color: inherit; }
-
diff --git a/docs/tocBullet.svg b/docs/tocBullet.svg
deleted file mode 100644
index 29abb51..0000000
--- a/docs/tocBullet.svg
+++ /dev/null
@@ -1,11 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Generator: Adobe Illustrator 19.2.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
-<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
-	 viewBox="0 0 16 280" style="enable-background:new 0 0 16 280;" xml:space="preserve">
-<style type="text/css">
-	.st0{fill:#FFFFFF;stroke:#5D9DD5;stroke-miterlimit:10;}
-	.st1{fill:#5D9DD5;}
-</style>
-<path class="st0" d="M10,25H6c-1.7,0-3-1.4-3-3v-4c0-1.6,1.3-3,3-3h4c1.6,0,3,1.4,3,3v4C13,23.6,11.6,25,10,25z"/>
-<path class="st1" d="M10,265H6c-1.7,0-3-1.3-3-3v-4c0-1.7,1.3-3,3-3h4c1.6,0,3,1.3,3,3v4C13,263.7,11.6,265,10,265z"/>
-</svg>

From c98b456791a1119640909b7e085b2c2dcdd9bd8f Mon Sep 17 00:00:00 2001
From: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Date: Fri, 10 Mar 2023 17:04:39 -0800
Subject: [PATCH 11/15] add alt-text

---
 vignettes/binary-data.Rmd     | 3 ++-
 vignettes/continuous-data.Rmd | 8 +++++---
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/vignettes/binary-data.Rmd b/vignettes/binary-data.Rmd
index 27b3fc1..17634db 100644
--- a/vignettes/binary-data.Rmd
+++ b/vignettes/binary-data.Rmd
@@ -68,7 +68,8 @@ jacc_sim
 
 As we can see below, this is a fairly diverse training set:
 
-```{r jac-plot, fig.width=5, fig.height=5.2,  out.width = '50%', fig.align='center'}
+```{r jac-plot}
+#| fig-alt: "Empirical cumulative distribution chart. Mean similarity along the x-axis, Cumulative Probability along the why axis. Reading from left to right, values stay close to 0 from x = 0 to x = 0.25, from x = 0.25 to x = 0.4 there is a near-linear upwards trend to about y = 0.70. After that y = 1."
 library(ggplot2)
 
 # Plot the empirical cumulative distribution function for the training set
diff --git a/vignettes/continuous-data.Rmd b/vignettes/continuous-data.Rmd
index ff8f47b..4fc5f6e 100644
--- a/vignettes/continuous-data.Rmd
+++ b/vignettes/continuous-data.Rmd
@@ -133,8 +133,8 @@ ames_pca
 
 Plotting the distribution function for the PCA scores is also helpful:
 
-```{r autoplot, fig.align='center'}
-
+```{r autoplot}
+#| fig-alt: "Faceted line chart. abs(value) along the x-axis, percentile along the y-axis. The facets are distance, followed by PC 1 through 12. All lines go up fairly fast."
 library(ggplot2)
 autoplot(ames_pca)
 ```
@@ -161,6 +161,7 @@ Notice how the samples, displayed in red, are fairly dissimilar to the training
 set in the first component:
 
 ```{r, echo = FALSE}
+#| fig-alt: "Histogram chart. PC001 along the x-axis, count along the y-axis. A vertical red line is placed inside the distribution."
 training_scores <- score(ames_pca, training_data)
 ggplot(training_scores, aes(x = PC001)) + 
   geom_histogram(col = "white", binwidth = .5) + 
@@ -181,7 +182,8 @@ These three houses are extreme in the most influential variable (year built) sin
 they were new homes. The also tend to have fairly large garages:
 
 ```{r, echo = FALSE}
-ggplot(training_data, aes(x = Garage_Area )) + 
+#| fig-alt: "Histogram chart. Garage_Area along the x-axis, count along the y-axis. Two red vertical lines are places. One on the edge of the distribution, another outside the distribution."
+ggplot(training_data, aes(x = Garage_Area)) + 
   geom_histogram(col = "white", binwidth = 50) + 
   geom_vline(xintercept = ames_new$Garage_Area, col = "red")
 ```

From b0321ecde444280b59c715cf0acd11208b23a166 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Fri, 17 Mar 2023 18:47:45 -0400
Subject: [PATCH 12/15] Create CNAME

---
 docs/CNAME | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 docs/CNAME

diff --git a/docs/CNAME b/docs/CNAME
new file mode 100644
index 0000000..944fc48
--- /dev/null
+++ b/docs/CNAME
@@ -0,0 +1 @@
+applicable.tidymodels.org
\ No newline at end of file

From 725706751476d5dcab902892db8b3e9fc8e7beb0 Mon Sep 17 00:00:00 2001
From: Teun van den Brand <tahvdbrand@gmail.com>
Date: Thu, 30 Jan 2025 13:22:10 +0100
Subject: [PATCH 13/15] use label helper

---
 tests/testthat/test-plot.R | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/tests/testthat/test-plot.R b/tests/testthat/test-plot.R
index f0d94e0..924b705 100644
--- a/tests/testthat/test-plot.R
+++ b/tests/testthat/test-plot.R
@@ -1,3 +1,8 @@
+get_labs <- function(x) x$labels
+if ("get_labs" %in% getNamespaceExports("ggplot2")) {
+  get_labs <- ggplot2::get_labs
+}
+
 test_that("output of autoplot.apd_pca is correct when no options are provided", {
   ad <- apd_pca(mtcars)
   ad_plot <- ggplot2::autoplot(ad)
@@ -6,8 +11,9 @@ test_that("output of autoplot.apd_pca is correct when no options are provided",
     tidyr::gather(component, value, -percentile)
 
   expect_equal(ad_plot$data, pctls)
-  expect_equal(ad_plot$labels$x, "abs(value)")
-  expect_equal(ad_plot$labels$y, "percentile")
+  labs <- get_labs(ad_plot)
+  expect_equal(labs$x, "abs(value)")
+  expect_equal(labs$y, "percentile")
 })
 
 test_that("output of autoplot.apd_pca is correct when options=matches are provided", {
@@ -19,8 +25,9 @@ test_that("output of autoplot.apd_pca is correct when options=matches are provid
     tidyr::gather(component, value, -percentile)
 
   expect_equal(ad_plot$data, pctls)
-  expect_equal(ad_plot$labels$x, "abs(value)")
-  expect_equal(ad_plot$labels$y, "percentile")
+  labs <- get_labs(ad_plot)
+  expect_equal(labs$x, "abs(value)")
+  expect_equal(labs$y, "percentile")
 })
 
 test_that("output of autoplot.apd_pca is correct when options=distance are provided", {
@@ -32,6 +39,7 @@ test_that("output of autoplot.apd_pca is correct when options=distance are provi
     tidyr::gather(component, value, -percentile)
 
   expect_equal(ad_plot$data, pctls)
-  expect_equal(ad_plot$labels$x, "abs(value)")
-  expect_equal(ad_plot$labels$y, "percentile")
+  labs <- get_labs(ad_plot)
+  expect_equal(labs$x, "abs(value)")
+  expect_equal(labs$y, "percentile")
 })

From 88769f22a2727eb951d54765840069eab1966064 Mon Sep 17 00:00:00 2001
From: Max Kuhn <mxkuhn@gmail.com>
Date: Mon, 16 Feb 2026 17:44:47 -0500
Subject: [PATCH 14/15] 2023 spring cleaning (#35)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* pkgdown and GHA updates

* usethis::use_tidy_style()

* usethis::use_tidy_description()

* regenerate snapshots with newer rlang

* rstudio -> posit, fix urls

* re-regenerate snapshots

* move imports to package docs

* udpate testthat and other pkg version

* update imports

* retrigger checks

* update GHA

---------

Co-authored-by: Marly Gotti <marlygotti@gmail.com>
Co-authored-by: ‘topepo’ <‘mxkuhn@gmail.com’>
---
 .Rbuildignore                             |  1 +
 .github/workflows/R-CMD-check.yaml        |  3 ++
 .github/workflows/pkgdown.yaml            | 11 ++++--
 .github/workflows/pr-commands.yaml        | 12 ++++--
 .github/workflows/test-coverage.yaml      | 28 ++++++++++----
 .gitignore                                |  1 +
 DESCRIPTION                               | 16 ++++----
 NAMESPACE                                 |  4 +-
 R/0.R                                     | 47 -----------------------
 R/applicable-package.R                    | 46 +++++++++++++++++++++-
 R/data.R                                  |  4 +-
 R/print.R                                 |  2 +-
 README.Rmd                                |  6 +--
 README.md                                 |  5 ++-
 _pkgdown.yml                              | 16 +++-----
 inst/WORDLIST                             | 23 ++++++-----
 man/ames_new.Rd                           |  2 +-
 man/okc_binary.Rd                         |  2 +-
 man/print.apd_similarity.Rd               |  2 +-
 tests/testthat/_snaps/hat_values-fit.md   | 20 ++++++----
 tests/testthat/_snaps/hat_values-score.md | 15 +++++---
 tests/testthat/_snaps/misc.md             |  5 ++-
 tests/testthat/_snaps/pca-fit.md          | 10 +++--
 tests/testthat/_snaps/pca-score.md        | 15 +++++---
 tests/testthat/_snaps/similarity.md       | 38 ++++++++++--------
 tests/testthat/test-hat_values-fit.R      | 12 ++++--
 tests/testthat/test-hat_values-score.R    | 12 ++++--
 tests/testthat/test-misc.R                |  3 +-
 tests/testthat/test-pca-fit.R             | 18 ++++++---
 tests/testthat/test-pca-score.R           | 18 ++++++---
 tests/testthat/test-similarity.R          | 22 +++++++----
 vignettes/continuous-data.Rmd             | 26 ++++++-------
 32 files changed, 254 insertions(+), 191 deletions(-)
 delete mode 100644 R/0.R

diff --git a/.Rbuildignore b/.Rbuildignore
index 14a6d3c..e60b238 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -12,3 +12,4 @@
 ^CODE_OF_CONDUCT\.md$
 ^\.github$
 ^codecov\.yml$
+^README\.Rmd$
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 4e06c17..ee65ccb 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -33,6 +33,9 @@ jobs:
           - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
           - {os: ubuntu-latest,   r: 'release'}
           - {os: ubuntu-latest,   r: 'oldrel-1'}
+          - {os: ubuntu-latest,   r: 'oldrel-2'}
+          - {os: ubuntu-latest,   r: 'oldrel-3'}
+          - {os: ubuntu-latest,   r: 'oldrel-4'}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index 087f0b0..bfc9f4d 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -4,12 +4,13 @@ on:
   push:
     branches: [main, master]
   pull_request:
-    branches: [main, master]
   release:
     types: [published]
   workflow_dispatch:
 
-name: pkgdown
+name: pkgdown.yaml
+
+permissions: read-all
 
 jobs:
   pkgdown:
@@ -19,8 +20,10 @@ jobs:
       group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-pandoc@v2
 
@@ -39,7 +42,7 @@ jobs:
 
       - name: Deploy to GitHub pages 🚀
         if: github.event_name != 'pull_request'
-        uses: JamesIves/github-pages-deploy-action@v4.4.1
+        uses: JamesIves/github-pages-deploy-action@v4.5.0
         with:
           clean: false
           branch: gh-pages
diff --git a/.github/workflows/pr-commands.yaml b/.github/workflows/pr-commands.yaml
index 71f335b..2edd93f 100644
--- a/.github/workflows/pr-commands.yaml
+++ b/.github/workflows/pr-commands.yaml
@@ -4,7 +4,9 @@ on:
   issue_comment:
     types: [created]
 
-name: Commands
+name: pr-commands.yaml
+
+permissions: read-all
 
 jobs:
   document:
@@ -13,8 +15,10 @@ jobs:
     runs-on: ubuntu-latest
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/pr-fetch@v2
         with:
@@ -50,8 +54,10 @@ jobs:
     runs-on: ubuntu-latest
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/pr-fetch@v2
         with:
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
index 2c5bb50..0ab748d 100644
--- a/.github/workflows/test-coverage.yaml
+++ b/.github/workflows/test-coverage.yaml
@@ -4,9 +4,10 @@ on:
   push:
     branches: [main, master]
   pull_request:
-    branches: [main, master]
 
-name: test-coverage
+name: test-coverage.yaml
+
+permissions: read-all
 
 jobs:
   test-coverage:
@@ -15,7 +16,7 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-r@v2
         with:
@@ -23,28 +24,39 @@ jobs:
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::covr
+          extra-packages: any::covr, any::xml2
           needs: coverage
 
       - name: Test coverage
         run: |
-          covr::codecov(
+          cov <- covr::package_coverage(
             quiet = FALSE,
             clean = FALSE,
-            install_path = file.path(Sys.getenv("RUNNER_TEMP"), "package")
+            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
           )
+          print(cov)
+          covr::to_cobertura(cov)
         shell: Rscript {0}
 
+      - uses: codecov/codecov-action@v5
+        with:
+          # Fail if error if not on PR, or if on PR and token is given
+          fail_ci_if_error: ${{ github.event_name != 'pull_request' || secrets.CODECOV_TOKEN }}
+          files: ./cobertura.xml
+          plugins: noop
+          disable_search: true
+          token: ${{ secrets.CODECOV_TOKEN }}
+
       - name: Show testthat output
         if: always()
         run: |
           ## --------------------------------------------------------------------
-          find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true
+          find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true
         shell: bash
 
       - name: Upload test results
         if: failure()
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: coverage-test-failures
           path: ${{ runner.temp }}/package
diff --git a/.gitignore b/.gitignore
index 66b86ae..a385dea 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,4 @@
 .Rproj.user
 .DS_Store
 inst/doc
+docs
diff --git a/DESCRIPTION b/DESCRIPTION
index 358abcd..fb35c12 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -4,7 +4,7 @@ Version: 0.0.1.1
 Authors@R: c(
     person("Marly", "Gotti", , "marlygotti@gmail.com", role = c("aut", "cre")),
     person("Max", "Kuhn", , "max@posit.co", role = "aut"),
-    person(given = "Posit Software, PBC", role = c("cph", "fnd"))
+    person("Posit Software, PBC", role = c("cph", "fnd"))
   )
 Description: A modeling package compiling applicability domain methods in
     R.  It combines different methods to measure the amount of
@@ -17,15 +17,15 @@ URL: https://github.com/tidymodels/applicable,
 BugReports: https://github.com/tidymodels/applicable/issues
 Depends: 
     ggplot2,
-    R (>= 3.4)
+    R (>= 3.6)
 Imports: 
     dplyr,
     glue,
-    hardhat (>= 0.1.2),
+    hardhat (>= 1.3.0),
     Matrix,
     proxyC,
     purrr,
-    rlang,
+    rlang (>= 1.1.1),
     stats,
     tibble,
     tidyr,
@@ -35,17 +35,17 @@ Suggests:
     covr,
     knitr,
     modeldata,
-    recipes (>= 0.1.7),
+    recipes (>= 1.0.8),
     rmarkdown,
     spelling,
-    testthat (>= 3.0.0),
+    testthat (>= 3.2.0),
     xml2
 VignetteBuilder: 
     knitr
+Config/Needs/website: tidyverse/tidytemplate
+Config/testthat/edition: 3
 Encoding: UTF-8
 Language: en-US
 LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
-Config/Needs/website: tidyverse/tidytemplate
-Config/testthat/edition: 3
diff --git a/NAMESPACE b/NAMESPACE
index 391f9ad..6cc910e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -31,6 +31,7 @@ export(autoplot.apd_pca)
 export(autoplot.apd_similarity)
 export(score)
 export(score.default)
+import(rlang)
 importFrom(Matrix,Matrix)
 importFrom(Matrix,colSums)
 importFrom(dplyr,"%>%")
@@ -59,9 +60,6 @@ importFrom(hardhat,validate_prediction_size)
 importFrom(proxyC,simil)
 importFrom(purrr,map2_dfc)
 importFrom(purrr,map_dfc)
-importFrom(rlang,abort)
-importFrom(rlang,arg_match)
-importFrom(rlang,enquos)
 importFrom(stats,approx)
 importFrom(stats,ecdf)
 importFrom(stats,prcomp)
diff --git a/R/0.R b/R/0.R
deleted file mode 100644
index 2c8adba..0000000
--- a/R/0.R
+++ /dev/null
@@ -1,47 +0,0 @@
-#' @importFrom dplyr %>%
-#' @importFrom dplyr select
-#' @importFrom dplyr slice
-#' @importFrom dplyr matches
-#' @importFrom dplyr starts_with
-#' @importFrom dplyr rename_all
-#' @importFrom dplyr mutate
-#' @importFrom dplyr mutate_all
-#' @importFrom dplyr group_by
-#' @importFrom dplyr ungroup
-#' @importFrom dplyr count
-#' @importFrom dplyr sample_n
-#' @importFrom glue glue
-#' @importFrom tibble as_tibble
-#' @importFrom tibble tibble
-#' @importFrom purrr map_dfc
-#' @importFrom purrr map2_dfc
-#' @importFrom rlang abort
-#' @importFrom rlang enquos
-#' @importFrom rlang arg_match
-#' @importFrom stats predict
-#' @importFrom stats prcomp
-#' @importFrom stats approx
-#' @importFrom stats quantile
-#' @importFrom stats ecdf
-#' @importFrom stats setNames
-#' @importFrom hardhat validate_prediction_size
-#' @importFrom hardhat forge
-#' @importFrom hardhat mold
-#' @importFrom hardhat new_model
-#' @importFrom ggplot2 ggplot geom_step xlab ylab aes autoplot
-#' @importFrom Matrix Matrix colSums
-#' @importFrom tidyselect vars_select
-#' @importFrom tidyr gather
-#' @importFrom proxyC simil
-
-# ------------------------------------------------------------------------------
-# nocov
-
-# Reduce false positives when R CMD check runs its "no visible binding for
-# global variable" check
-#' @importFrom utils globalVariables
-utils::globalVariables(
-  c("cumulative", "n", "sim", "percentile", "component", "value")
-)
-
-# nocov end
diff --git a/R/applicable-package.R b/R/applicable-package.R
index b30bbda..ff213e8 100644
--- a/R/applicable-package.R
+++ b/R/applicable-package.R
@@ -1,8 +1,50 @@
 #' @keywords internal
 "_PACKAGE"
 
-# The following block is used by usethis to automatically manage
-# roxygen namespace tags. Modify with care!
 ## usethis namespace: start
+
+#' @import rlang
+#' @importFrom dplyr %>%
+#' @importFrom dplyr count
+#' @importFrom dplyr group_by
+#' @importFrom dplyr matches
+#' @importFrom dplyr mutate
+#' @importFrom dplyr mutate_all
+#' @importFrom dplyr rename_all
+#' @importFrom dplyr sample_n
+#' @importFrom dplyr select
+#' @importFrom dplyr slice
+#' @importFrom dplyr starts_with
+#' @importFrom dplyr ungroup
+#' @importFrom ggplot2 ggplot geom_step xlab ylab aes autoplot
+#' @importFrom glue glue
+#' @importFrom hardhat forge
+#' @importFrom hardhat mold
+#' @importFrom hardhat new_model
+#' @importFrom hardhat validate_prediction_size
+#' @importFrom Matrix Matrix colSums
+#' @importFrom proxyC simil
+#' @importFrom purrr map_dfc
+#' @importFrom purrr map2_dfc
+#' @importFrom stats approx
+#' @importFrom stats ecdf
+#' @importFrom stats prcomp
+#' @importFrom stats predict
+#' @importFrom stats quantile
+#' @importFrom stats setNames
+#' @importFrom tibble as_tibble
+#' @importFrom tibble tibble
+#' @importFrom tidyr gather
+#' @importFrom tidyselect vars_select
+#' @importFrom utils globalVariables
 ## usethis namespace: end
+
+# ------------------------------------------------------------------------------
+# global variable" check
+# nocov
+# nocov end
+# Reduce false positives when R CMD check runs its "no visible binding for
+utils::globalVariables(
+  c("cumulative", "n", "sim", "percentile", "component", "value")
+)
 NULL
diff --git a/R/data.R b/R/data.R
index 0c5bcee..a530a3c 100644
--- a/R/data.R
+++ b/R/data.R
@@ -30,7 +30,7 @@ NULL
 #' @return \item{okc_binary_train,okc_binary_test}{data frame frames with 61 columns}
 #'
 #' @source
-#' Kim (2015), "OkCupid Data for Introductory Statistics and Data Science Courses", _Journal of Statistics Education_, Volume 23, Number 2. \url{https://www.tandfonline.com/doi/abs/10.1080/10691898.2015.11889737}
+#' Kim (2015), "OkCupid Data for Introductory Statistics and Data Science Courses", _Journal of Statistics Education_, Volume 23, Number 2. \doi{10.1080/10691898.2015.11889737}
 #'
 #' Kuhn and Johnson (2020), _Feature Engineering and Selection_, Chapman and Hall/CRC . \url{https://bookdown.org/max/FES/} and \url{https://github.com/topepo/FES}
 #'
@@ -60,7 +60,7 @@ NULL
 #' Data as an End of Semester Regression Project," \emph{Journal of Statistics
 #' Education},  Volume 19, Number 3.
 #'
-#' \url{https://www.cityofames.org/government/departments-divisions-a-h/city-assessor}
+#' `https://www.cityofames.org/government/departments-divisions-a-h/city-assessor`
 #'
 #' \url{http://jse.amstat.org/v19n3/decock/DataDocumentation.txt}
 #'
diff --git a/R/print.R b/R/print.R
index 259eaf4..2cd14d8 100644
--- a/R/print.R
+++ b/R/print.R
@@ -74,7 +74,7 @@ print.apd_hat_values <- function(x, ...) {
 #' tr_x <- matrix(
 #'   sample(0:1, size = 20 * 50, prob = rep(.5, 2), replace = TRUE),
 #'   ncol = 20
-#'  )
+#' )
 #' model <- apd_similarity(tr_x)
 #' print(model)
 #' @export
diff --git a/README.Rmd b/README.Rmd
index 709b57a..ddaa0f3 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -15,16 +15,16 @@ knitr::opts_chunk$set(
 )
 
 options(rlang__backtrace_on_error = "reminder")
-
 ```
 
 # applicable <img src="man/figures/logo.png" align="right"/>
 
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/tidymodels/applicable/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidymodels/applicable/actions/workflows/R-CMD-check.yaml)
-[![Codecov test coverage](https://codecov.io/gh/tidymodels/applicable/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidymodels/applicable?branch=main)
+[![Codecov test coverage](https://codecov.io/gh/tidymodels/applicable/graph/badge.svg)](https://app.codecov.io/gh/tidymodels/applicable)
 [![Lifecycle:experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
 [![CRAN status](https://www.r-pkg.org/badges/version/applicable)](https://cran.r-project.org/package=applicable)
+
 <!-- badges: end -->
 
 ## Introduction
@@ -58,7 +58,7 @@ To learn about how to use applicable, check out the vignettes:
 
 This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
 
-- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on RStudio Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
+- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on Posit Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
 
 - If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/applicable/issues).
 
diff --git a/README.md b/README.md
index 5fbc494..7a3e2b4 100644
--- a/README.md
+++ b/README.md
@@ -7,10 +7,11 @@
 
 [![R-CMD-check](https://github.com/tidymodels/applicable/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidymodels/applicable/actions/workflows/R-CMD-check.yaml)
 [![Codecov test
-coverage](https://codecov.io/gh/tidymodels/applicable/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidymodels/applicable?branch=main)
+coverage](https://codecov.io/gh/tidymodels/applicable/graph/badge.svg)](https://app.codecov.io/gh/tidymodels/applicable)
 [![Lifecycle:experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/applicable)](https://cran.r-project.org/package=applicable)
+
 <!-- badges: end -->
 
 ## Introduction
@@ -57,7 +58,7 @@ Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
 By contributing to this project, you agree to abide by its terms.
 
 - For questions and discussions about tidymodels packages, modeling, and
-  machine learning, please [post on RStudio
+  machine learning, please [post on Posit
   Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
 
 - If you think you have encountered a bug, please [submit an
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 28b49fe..b7a660d 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,21 +1,15 @@
-url: https://applicable.tidymodels.org
-
+url: https://applicable.tidymodels.org/
 template:
   package: tidytemplate
   bootstrap: 5
   bslib:
-    primary: "#CA225E"
-
+    primary: '#CA225E'
   includes:
-      in_header: |
-        <script defer data-domain="applicable.tidymodels.org,all.tidymodels.org" src="https://plausible.io/js/plausible.js"></script>
-
-# https://github.com/tidyverse/tidytemplate for css
-
+    in_header: |
+      <script defer data-domain="applicable.tidymodels.org,all.tidymodels.org" src="https://plausible.io/js/plausible.js"></script>
 development:
   mode: auto
-
-
 figures:
   fig.width: 8
   fig.height: 5.75
+
diff --git a/inst/WORDLIST b/inst/WORDLIST
index fa3b318..a0cc3cd 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -1,24 +1,27 @@
-al
 Ames
-Chemoinformatics
+CMD
 CRC
+Chemoinformatics
+Codecov
 De
-doi
-et
-extensibility
 Gillet
 Jaccard
 Lifecycle
 Netzeva
 OkCupid
-pca
-pcas
-pre
+PBC
 QSAR
 Springer
-tibble
 X'X
+al
+doi
+et
+extensibility
+funder
 intercal
-CMD
+pca
+pcas
+pre
 reprex
+tibble
 tidymodels
diff --git a/man/ames_new.Rd b/man/ames_new.Rd
index bf239a1..0124f25 100644
--- a/man/ames_new.Rd
+++ b/man/ames_new.Rd
@@ -9,7 +9,7 @@ De Cock, D. (2011). "Ames, Iowa: Alternative to the Boston Housing
 Data as an End of Semester Regression Project," \emph{Journal of Statistics
 Education},  Volume 19, Number 3.
 
-\url{https://www.cityofames.org/government/departments-divisions-a-h/city-assessor}
+\verb{https://www.cityofames.org/government/departments-divisions-a-h/city-assessor}
 
 \url{http://jse.amstat.org/v19n3/decock/DataDocumentation.txt}
 
diff --git a/man/okc_binary.Rd b/man/okc_binary.Rd
index dce9dce..35ad4c7 100644
--- a/man/okc_binary.Rd
+++ b/man/okc_binary.Rd
@@ -7,7 +7,7 @@
 \alias{okc_binary_test}
 \title{OkCupid Binary Predictors}
 \source{
-Kim (2015), "OkCupid Data for Introductory Statistics and Data Science Courses", \emph{Journal of Statistics Education}, Volume 23, Number 2. \url{https://www.tandfonline.com/doi/abs/10.1080/10691898.2015.11889737}
+Kim (2015), "OkCupid Data for Introductory Statistics and Data Science Courses", \emph{Journal of Statistics Education}, Volume 23, Number 2. \doi{10.1080/10691898.2015.11889737}
 
 Kuhn and Johnson (2020), \emph{Feature Engineering and Selection}, Chapman and Hall/CRC . \url{https://bookdown.org/max/FES/} and \url{https://github.com/topepo/FES}
 }
diff --git a/man/print.apd_similarity.Rd b/man/print.apd_similarity.Rd
index 5b95bd4..31f782b 100644
--- a/man/print.apd_similarity.Rd
+++ b/man/print.apd_similarity.Rd
@@ -23,7 +23,7 @@ set.seed(535)
 tr_x <- matrix(
   sample(0:1, size = 20 * 50, prob = rep(.5, 2), replace = TRUE),
   ncol = 20
- )
+)
 model <- apd_similarity(tr_x)
 print(model)
 }
diff --git a/tests/testthat/_snaps/hat_values-fit.md b/tests/testthat/_snaps/hat_values-fit.md
index 779501b..5cd8d25 100644
--- a/tests/testthat/_snaps/hat_values-fit.md
+++ b/tests/testthat/_snaps/hat_values-fit.md
@@ -2,29 +2,33 @@
 
     Code
       new_apd_hat_values(blueprint = hardhat::default_xy_blueprint())
-    Error <simpleError>
-      argument "XtX_inv" is missing, with no default
+    Condition
+      Error in `new_apd_hat_values()`:
+      ! argument "XtX_inv" is missing, with no default
 
 # `new_apd_hat_values` fails when blueprint is numeric
 
     Code
       new_apd_hat_values(XtX_inv = 1, blueprint = 1)
-    Error <rlang_error>
-      blueprint should be a blueprint, not a numeric.
+    Condition
+      Error in `hardhat::new_model()`:
+      ! `blueprint` must be a <hardhat_blueprint>, not the number 1.
 
 # `apd_hat_values` fails when matrix has more predictors than samples
 
     Code
       apd_hat_values(bad_data)
-    Error <rlang_error>
-      The number of columns must be less than the number of rows.
+    Condition
+      Error in `apd_hat_values_bridge()`:
+      ! The number of columns must be less than the number of rows.
 
 # `apd_hat_values` fails when the matrix X^tX is singular
 
     Code
       apd_hat_values(bad_data)
-    Error <rlang_error>
-      Unable to compute the hat values of the matrix X of
+    Condition
+      Error in `get_inv()`:
+      ! Unable to compute the hat values of the matrix X of
       predictors because the matrix resulting from multiplying
       the transpose of X by X is singular.
 
diff --git a/tests/testthat/_snaps/hat_values-score.md b/tests/testthat/_snaps/hat_values-score.md
index b3cbc4d..1c8e939 100644
--- a/tests/testthat/_snaps/hat_values-score.md
+++ b/tests/testthat/_snaps/hat_values-score.md
@@ -2,22 +2,25 @@
 
     Code
       score_apd_hat_values_numeric(mtcars, mtcars)
-    Error <rlang_error>
-      The model must contain an XtX_inv argument.
+    Condition
+      Error in `score_apd_hat_values_numeric()`:
+      ! The model must contain an XtX_inv argument.
 
 # `score` fails when predictors only contain factors
 
     Code
       score(model, iris$Species)
-    Error <rlang_error>
-      The class of `new_data`, 'factor', is not recognized.
+    Condition
+      Error in `hardhat::forge()`:
+      ! The class of `new_data`, 'factor', is not recognized.
 
 # `score` fails when predictors are vectors
 
     Code
       score(object)
-    Error <rlang_error>
-      `object` is not of a recognized type.
+    Condition
+      Error in `score()`:
+      ! `object` is not of a recognized type.
       Only data.frame, matrix, recipe, and formula objects are allowed.
       A data.frame was specified.
 
diff --git a/tests/testthat/_snaps/misc.md b/tests/testthat/_snaps/misc.md
index 68860d0..247bf44 100644
--- a/tests/testthat/_snaps/misc.md
+++ b/tests/testthat/_snaps/misc.md
@@ -2,6 +2,7 @@
 
     Code
       names0(num)
-    Error <rlang_error>
-      `num` should be > 0
+    Condition
+      Error in `names0()`:
+      ! `num` should be > 0
 
diff --git a/tests/testthat/_snaps/pca-fit.md b/tests/testthat/_snaps/pca-fit.md
index d66228e..2ef90b2 100644
--- a/tests/testthat/_snaps/pca-fit.md
+++ b/tests/testthat/_snaps/pca-fit.md
@@ -2,13 +2,15 @@
 
     Code
       new_apd_pca(blueprint = hardhat::default_xy_blueprint())
-    Error <simpleError>
-      argument "pcs" is missing, with no default
+    Condition
+      Error in `new_apd_pca()`:
+      ! argument "pcs" is missing, with no default
 
 # `new_apd_pca` fails when blueprint is numeric
 
     Code
       new_apd_pca(pcs = 1, blueprint = 1)
-    Error <rlang_error>
-      blueprint should be a blueprint, not a numeric.
+    Condition
+      Error in `hardhat::new_model()`:
+      ! `blueprint` must be a <hardhat_blueprint>, not the number 1.
 
diff --git a/tests/testthat/_snaps/pca-score.md b/tests/testthat/_snaps/pca-score.md
index 30ac29f..75b58b7 100644
--- a/tests/testthat/_snaps/pca-score.md
+++ b/tests/testthat/_snaps/pca-score.md
@@ -2,22 +2,25 @@
 
     Code
       score_apd_pca_numeric(mtcars, mtcars)
-    Error <rlang_error>
-      The model must contain a pcs argument.
+    Condition
+      Error in `score_apd_pca_numeric()`:
+      ! The model must contain a pcs argument.
 
 # `score` fails when predictors only contain factors
 
     Code
       score(model, iris$Species)
-    Error <rlang_error>
-      The class of `new_data`, 'factor', is not recognized.
+    Condition
+      Error in `hardhat::forge()`:
+      ! The class of `new_data`, 'factor', is not recognized.
 
 # `score` fails when predictors are vectors
 
     Code
       score(object)
-    Error <rlang_error>
-      `object` is not of a recognized type.
+    Condition
+      Error in `score()`:
+      ! `object` is not of a recognized type.
       Only data.frame, matrix, recipe, and formula objects are allowed.
       A data.frame was specified.
 
diff --git a/tests/testthat/_snaps/similarity.md b/tests/testthat/_snaps/similarity.md
index abb0705..afde47b 100644
--- a/tests/testthat/_snaps/similarity.md
+++ b/tests/testthat/_snaps/similarity.md
@@ -2,15 +2,17 @@
 
     Code
       apd_similarity(tr_x, quantile = 2)
-    Error <rlang_error>
-      The `quantile` argument should be NA or a single numeric value in [0, 1].
+    Condition
+      Error in `apd_similarity_bridge()`:
+      ! The `quantile` argument should be NA or a single numeric value in [0, 1].
 
 ---
 
     Code
       apd_similarity(tr_x_sp)
-    Error <rlang_error>
-      `x` is not of a recognized type.
+    Condition
+      Error in `apd_similarity()`:
+      ! `x` is not of a recognized type.
       Only data.frame, matrix, recipe, and formula objects are allowed.
       A dgCMatrix was specified.
 
@@ -54,28 +56,32 @@
 
     Code
       apd_similarity(tr_x, quantile = -1)
-    Error <rlang_error>
-      The `quantile` argument should be NA or a single numeric value in [0, 1].
+    Condition
+      Error in `apd_similarity_bridge()`:
+      ! The `quantile` argument should be NA or a single numeric value in [0, 1].
 
 ---
 
     Code
       apd_similarity(tr_x, quantile = 3)
-    Error <rlang_error>
-      The `quantile` argument should be NA or a single numeric value in [0, 1].
+    Condition
+      Error in `apd_similarity_bridge()`:
+      ! The `quantile` argument should be NA or a single numeric value in [0, 1].
 
 ---
 
     Code
       apd_similarity(tr_x, quantile = "la")
-    Error <rlang_error>
-      The `quantile` argument should be NA or a single numeric value in [0, 1].
+    Condition
+      Error in `apd_similarity_bridge()`:
+      ! The `quantile` argument should be NA or a single numeric value in [0, 1].
 
 # apd_similarity outputs warning with zero variance variables 
 
     Code
       apd_similarity(bad_data)
-    Warning <rlang_warning>
+    Condition
+      Warning:
       The following variables had zero variance and were removed: a, b, and d
     Output
       Applicability domain via similarity
@@ -86,13 +92,15 @@
 
     Code
       apd_similarity(bad_data)
-    Error <rlang_error>
-      All variables have a single unique value.
+    Condition
+      Error in `apd_similarity_bridge()`:
+      ! All variables have a single unique value.
 
 # apd_similarity fails data is not binary
 
     Code
       apd_similarity(bad_data)
-    Error <rlang_error>
-      The following variables are not binary: b, and d
+    Condition
+      Error in `apd_similarity_bridge()`:
+      ! The following variables are not binary: b, and d
 
diff --git a/tests/testthat/test-hat_values-fit.R b/tests/testthat/test-hat_values-fit.R
index 5837809..bd8cfc0 100644
--- a/tests/testthat/test-hat_values-fit.R
+++ b/tests/testthat/test-hat_values-fit.R
@@ -12,13 +12,15 @@ test_that("`new_apd_hat_values` arguments are assigned correctly", {
 })
 
 test_that("XtX_inv is provided", {
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     new_apd_hat_values(blueprint = hardhat::default_xy_blueprint())
   )
 })
 
 test_that("`new_apd_hat_values` fails when blueprint is numeric", {
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     new_apd_hat_values(XtX_inv = 1, blueprint = 1)
   )
 })
@@ -108,7 +110,8 @@ test_that("`apd_hat_values` fails when matrix has more predictors than samples",
   bad_data <- mtcars %>%
     slice(1:5)
 
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     apd_hat_values(bad_data)
   )
 })
@@ -120,7 +123,8 @@ test_that("`apd_hat_values` fails when the matrix X^tX is singular", {
   )
   colnames(bad_data) <- c("A", "B")
 
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     apd_hat_values(bad_data)
   )
 })
diff --git a/tests/testthat/test-hat_values-score.R b/tests/testthat/test-hat_values-score.R
index 84c0ed2..dacfd89 100644
--- a/tests/testthat/test-hat_values-score.R
+++ b/tests/testthat/test-hat_values-score.R
@@ -1,12 +1,14 @@
 test_that("`score_apd_hat_values_numeric` fails when model has no pcs argument", {
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     score_apd_hat_values_numeric(mtcars, mtcars)
   )
 })
 
 test_that("`score` fails when predictors only contain factors", {
   model <- apd_hat_values(~., iris)
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     score(model, iris$Species)
   )
 })
@@ -14,7 +16,8 @@ test_that("`score` fails when predictors only contain factors", {
 test_that("`score` fails when predictors are vectors", {
   object <- iris
 
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     score(object)
   )
 })
@@ -30,7 +33,8 @@ test_that("`score` calculated hat_values are correct", {
   actual_output <- actual_output$hat_values
 
   # Data frame method
-  expect_equal(ignore_attr = TRUE,
+  expect_equal(
+    ignore_attr = TRUE,
     actual_output,
     expected
   )
diff --git a/tests/testthat/test-misc.R b/tests/testthat/test-misc.R
index 4aef57f..d27b823 100644
--- a/tests/testthat/test-misc.R
+++ b/tests/testthat/test-misc.R
@@ -1,6 +1,7 @@
 test_that("`names0` fails if `num` is less than 1", {
   num <- 0
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     names0(num)
   )
 })
diff --git a/tests/testthat/test-pca-fit.R b/tests/testthat/test-pca-fit.R
index ace00bf..8901c7a 100644
--- a/tests/testthat/test-pca-fit.R
+++ b/tests/testthat/test-pca-fit.R
@@ -18,13 +18,15 @@ test_that("`new_apd_pca` arguments are assigned correctly", {
 })
 
 test_that("pcs is provided", {
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     new_apd_pca(blueprint = hardhat::default_xy_blueprint())
   )
 })
 
 test_that("`new_apd_pca` fails when blueprint is numeric", {
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     new_apd_pca(pcs = 1, blueprint = 1)
   )
 })
@@ -58,7 +60,8 @@ test_that("pcs matches `prcomp` output for the data frame method", {
   expected$x <- NULL
 
   # Data frame method
-  expect_equal(ignore_attr = TRUE,
+  expect_equal(
+    ignore_attr = TRUE,
     apd_pca(mtcars)$pcs,
     expected
   )
@@ -69,7 +72,8 @@ test_that("pcs matches `prcomp` output for the formula method", {
   expected$x <- NULL
 
   # Formula method
-  expect_equal(ignore_attr = TRUE,
+  expect_equal(
+    ignore_attr = TRUE,
     apd_pca(~., mtcars)$pcs,
     expected
   )
@@ -81,7 +85,8 @@ test_that("pcs matches `prcomp` output for the recipe method", {
 
   # Recipe method
   rec <- recipes::recipe(~., mtcars)
-  expect_equal(ignore_attr = TRUE,
+  expect_equal(
+    ignore_attr = TRUE,
     apd_pca(rec, data = mtcars)$pcs,
     expected
   )
@@ -92,7 +97,8 @@ test_that("pcs matches `prcomp` output for the matrix method", {
   expected$x <- NULL
 
   # Matrix method
-  expect_equal(ignore_attr = TRUE,
+  expect_equal(
+    ignore_attr = TRUE,
     apd_pca(as.matrix(mtcars))$pcs,
     expected
   )
diff --git a/tests/testthat/test-pca-score.R b/tests/testthat/test-pca-score.R
index 3a20844..8a12a00 100644
--- a/tests/testthat/test-pca-score.R
+++ b/tests/testthat/test-pca-score.R
@@ -1,12 +1,14 @@
 test_that("`score_apd_pca_numeric` fails when model has no pcs argument", {
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     score_apd_pca_numeric(mtcars, mtcars)
   )
 })
 
 test_that("`score` fails when predictors only contain factors", {
   model <- apd_pca(~., iris)
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     score(model, iris$Species)
   )
 })
@@ -14,7 +16,8 @@ test_that("`score` fails when predictors only contain factors", {
 test_that("`score` fails when predictors are vectors", {
   object <- iris
 
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     score(object)
   )
 })
@@ -31,7 +34,8 @@ test_that("`score_apd_pca_numeric` pcs output matches `stats::predict` output",
     dplyr::select(dplyr::matches("^PC\\d+$"))
 
   # Data frame method
-  expect_equal(ignore_attr = TRUE,
+  expect_equal(
+    ignore_attr = TRUE,
     actual_output,
     expected
   )
@@ -49,7 +53,8 @@ test_that("`score` pcs output matches `stats::predict` output", {
     dplyr::select(dplyr::matches("^PC\\d+$"))
 
   # Data frame method
-  expect_equal(ignore_attr = TRUE,
+  expect_equal(
+    ignore_attr = TRUE,
     actual_output,
     expected
   )
@@ -67,7 +72,8 @@ test_that("`score_apd_pca_bridge` output is correct", {
     dplyr::select(dplyr::matches("^PC\\d+$"))
 
   # Data frame method
-  expect_equal(ignore_attr = TRUE,
+  expect_equal(
+    ignore_attr = TRUE,
     actual_output,
     expected
   )
diff --git a/tests/testthat/test-similarity.R b/tests/testthat/test-similarity.R
index 08b0788..d6eb8e0 100644
--- a/tests/testthat/test-similarity.R
+++ b/tests/testthat/test-similarity.R
@@ -114,10 +114,12 @@ test_that("matrix method - quantile similarity", {
 # ------------------------------------------------------------------------------
 
 test_that("bad args", {
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     apd_similarity(tr_x, quantile = 2)
   )
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     apd_similarity(tr_x_sp)
   )
 })
@@ -143,16 +145,18 @@ test_that("plot output", {
 # ------------------------------------------------------------------------------
 
 test_that("apd_similarity fails when quantile is neither NA nor a number in [0, 1]", {
-
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     apd_similarity(tr_x, quantile = -1)
   )
 
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     apd_similarity(tr_x, quantile = 3)
   )
 
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     apd_similarity(tr_x, quantile = "la")
   )
 })
@@ -183,7 +187,8 @@ test_that("apd_similarity fails when all the variables have zero variance", {
   )
   bad_data <- as.data.frame(bad_data)
 
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     apd_similarity(bad_data)
   )
 })
@@ -199,7 +204,8 @@ test_that("apd_similarity fails data is not binary", {
   )
   bad_data <- as.data.frame(bad_data)
 
-  expect_snapshot(error = TRUE,
+  expect_snapshot(
+    error = TRUE,
     apd_similarity(bad_data)
   )
 })
diff --git a/vignettes/continuous-data.Rmd b/vignettes/continuous-data.Rmd
index 4fc5f6e..89de9bd 100644
--- a/vignettes/continuous-data.Rmd
+++ b/vignettes/continuous-data.Rmd
@@ -71,10 +71,10 @@ library(dplyr)
 
 ames_cols <- intersect(names(ames), names(ames_new))
 
-training_data <- 
-  ames %>% 
+training_data <-
+  ames %>%
   # For consistency, only analyze the data on new properties
-  dplyr::select(one_of(ames_cols)) %>% 
+  dplyr::select(one_of(ames_cols)) %>%
   mutate(
     # There is a new neighborhood in ames_new
     Neighborhood = as.character(Neighborhood),
@@ -83,10 +83,10 @@ training_data <-
 
 
 training_recipe <-
-  recipe( ~ ., data = training_data) %>%
-  step_dummy(all_nominal()) %>% 
+  recipe(~., data = training_data) %>%
+  step_dummy(all_nominal()) %>%
   # Remove variables that have the same value for every data point.
-  step_zv(all_predictors()) %>% 
+  step_zv(all_predictors()) %>%
   # Transform variables to be distributed as Gaussian-like as possible.
   step_YeoJohnson(all_numeric()) %>%
   # Normalize numeric data to have a mean of zero and
@@ -163,8 +163,8 @@ set in the first component:
 ```{r, echo = FALSE}
 #| fig-alt: "Histogram chart. PC001 along the x-axis, count along the y-axis. A vertical red line is placed inside the distribution."
 training_scores <- score(ames_pca, training_data)
-ggplot(training_scores, aes(x = PC001)) + 
-  geom_histogram(col = "white", binwidth = .5) + 
+ggplot(training_scores, aes(x = PC001)) +
+  geom_histogram(col = "white", binwidth = .5) +
   geom_vline(xintercept = pca_score$PC001, col = "red")
 ```
 
@@ -183,8 +183,8 @@ they were new homes. The also tend to have fairly large garages:
 
 ```{r, echo = FALSE}
 #| fig-alt: "Histogram chart. Garage_Area along the x-axis, count along the y-axis. Two red vertical lines are places. One on the edge of the distribution, another outside the distribution."
-ggplot(training_data, aes(x = Garage_Area)) + 
-  geom_histogram(col = "white", binwidth = 50) + 
+ggplot(training_data, aes(x = Garage_Area)) +
+  geom_histogram(col = "white", binwidth = 50) +
   geom_vline(xintercept = ames_new$Garage_Area, col = "red")
 ```
 
@@ -221,8 +221,8 @@ Two caveats for using the hat values:
 Let us apply `apd_hat_values` modeling function to our data (while ensuring that there are no linear dependencies):
 
 ```{r}
-non_singular_recipe <- 
-  training_recipe %>% 
+non_singular_recipe <-
+  training_recipe %>%
   step_lincomb(all_predictors())
 
 # Recipe interface
@@ -231,7 +231,5 @@ ames_hat <- apd_hat_values(non_singular_recipe, training_data)
 
 
 ```{r reset_options}
-
 options(prev_options)
-
 ```

From d9761e33c8e13cce2f4849eabf268ceebab617c9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <‘mxkuhn@gmail.com’>
Date: Mon, 16 Feb 2026 20:03:37 -0500
Subject: [PATCH 15/15] regenerate snapshots

---
 DESCRIPTION                               | 4 ++--
 tests/testthat/_snaps/hat_values-score.md | 2 +-
 tests/testthat/_snaps/pca-score.md        | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index fb35c12..d74046c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -16,7 +16,7 @@ URL: https://github.com/tidymodels/applicable,
     https://applicable.tidymodels.org
 BugReports: https://github.com/tidymodels/applicable/issues
 Depends: 
-    ggplot2,
+    ggplot2 (>= 4.0.2),
     R (>= 3.6)
 Imports: 
     dplyr,
@@ -48,4 +48,4 @@ Encoding: UTF-8
 Language: en-US
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.3
diff --git a/tests/testthat/_snaps/hat_values-score.md b/tests/testthat/_snaps/hat_values-score.md
index 1c8e939..f941127 100644
--- a/tests/testthat/_snaps/hat_values-score.md
+++ b/tests/testthat/_snaps/hat_values-score.md
@@ -12,7 +12,7 @@
       score(model, iris$Species)
     Condition
       Error in `hardhat::forge()`:
-      ! The class of `new_data`, 'factor', is not recognized.
+      ! No `forge()` method provided for a <factor> object.
 
 # `score` fails when predictors are vectors
 
diff --git a/tests/testthat/_snaps/pca-score.md b/tests/testthat/_snaps/pca-score.md
index 75b58b7..b1d00d9 100644
--- a/tests/testthat/_snaps/pca-score.md
+++ b/tests/testthat/_snaps/pca-score.md
@@ -12,7 +12,7 @@
       score(model, iris$Species)
     Condition
       Error in `hardhat::forge()`:
-      ! The class of `new_data`, 'factor', is not recognized.
+      ! No `forge()` method provided for a <factor> object.
 
 # `score` fails when predictors are vectors