Package: applicable 0.2.1

Marly Gotti

applicable: A Compilation of Applicability Domain Methods

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 <doi:10.4018/IJQSPR.2016010102> for an overview of applicability domains.

Authors:Marly Gotti [aut, cre], Max Kuhn [aut], Posit Software, PBC [cph, fnd]

applicable_0.2.1.tar.gz
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applicable_0.2.1.tgz(r-4.6-any)applicable_0.2.1.tgz(r-4.5-any)
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applicable_0.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
applicable/json (API)

# Install 'applicable' in R:

install.packages('applicable', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tidymodels/applicable/issues

Pkgdown/docs site:https://applicable.tidymodels.org

Datasets:

ames_new - Recent Ames Iowa Houses
binary_tr - Binary QSAR Data
binary_unk - Binary QSAR Data
okc_binary_test - OkCupid Binary Predictors
okc_binary_train - OkCupid Binary Predictors

On CRAN:

7.22 score 47 stars 1 packages 59 scripts 3.3k downloads 8 exports 36 dependencies

Last updated from:5bc763e281. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	171
source / vignettes	OK	203
linux-release-x86_64	OK	176
macos-release-arm64	OK	133
macos-oldrel-arm64	OK	152
windows-devel	OK	198
windows-release	OK	138
windows-oldrel	OK	119
wasm-release	OK	130

Exports:apd_hat_values apd_isolation apd_pca apd_similarity autoplot.apd_pca autoplot.apd_similarity score score.default

Dependencies:cli cpp11 dplyr farver generics ggplot2 glue gtable hardhat isoband labeling lattice lifecycle magrittr Matrix pillar pkgconfig proxyC purrr R6 RColorBrewer Rcpp RcppArmadillo rlang S7 scales sparsevctrs stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Recent Ames Iowa Houses	ames_new
Fit a 'apd_hat_values'	apd_hat_values apd_hat_values.data.frame apd_hat_values.default apd_hat_values.formula apd_hat_values.matrix apd_hat_values.recipe
Fit an isolation forest to estimate an applicability domain.	apd_isolation apd_isolation.data.frame apd_isolation.default apd_isolation.formula apd_isolation.matrix apd_isolation.recipe
Fit a 'apd_pca'	apd_pca apd_pca.data.frame apd_pca.default apd_pca.formula apd_pca.matrix apd_pca.recipe
Applicability domain methods using binary similarity analysis	apd_similarity apd_similarity.data.frame apd_similarity.default apd_similarity.formula apd_similarity.matrix apd_similarity.recipe
Plot the distribution function for principal components	autoplot.apd_pca
Plot the cumulative distribution function for similarity metrics	autoplot.apd_similarity
Binary QSAR Data	binary binary_tr binary_unk qsar_binary
OkCupid Binary Predictors	okc_binary okc_binary_test okc_binary_train
Print number of predictors and principal components used.	print.apd_hat_values
Print number of predictors and principal components used.	print.apd_pca
Print number of predictors and principal components used.	print.apd_similarity
A scoring function	score score.default
Score new samples using hat values	score.apd_hat_values
Predict from a 'apd_isolation'	score.apd_isolation
Predict from a 'apd_pca'	score.apd_pca
Score new samples using similarity methods	score.apd_similarity