Package: applicable 0.0.1.1
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 Netzeva et al (2005) <doi:10.1177/026119290503300209> for an overview of applicability domains.
Authors:
applicable_0.0.1.1.tar.gz
applicable_0.0.1.1.zip(r-4.5)applicable_0.0.1.1.zip(r-4.4)applicable_0.0.1.1.zip(r-4.3)
applicable_0.0.1.1.tgz(r-4.4-any)applicable_0.0.1.1.tgz(r-4.3-any)
applicable_0.0.1.1.tar.gz(r-4.5-noble)applicable_0.0.1.1.tar.gz(r-4.4-noble)
applicable_0.0.1.1.tgz(r-4.4-emscripten)applicable_0.0.1.1.tgz(r-4.3-emscripten)
applicable.pdf |applicable.html✨
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
- 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
Last updated 2 years agofrom:b0321ecde4. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 16 2024 |
| R-4.5-win | OK | Nov 16 2024 |
| R-4.5-linux | OK | Nov 16 2024 |
| R-4.4-win | OK | Nov 16 2024 |
| R-4.4-mac | OK | Nov 16 2024 |
| R-4.3-win | OK | Nov 16 2024 |
| R-4.3-mac | OK | Nov 16 2024 |
Exports:apd_hat_valuesapd_pcaapd_similarityautoplot.apd_pcaautoplot.apd_similarityscorescore.default
Dependencies:clicolorspacecpp11dplyrfansifarvergenericsggplot2gluegtablehardhatisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigproxyCpurrrR6RColorBrewerRcppRcppArmadillorlangscalessparsevctrsstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr
Applicability domain methods for binary data
Rendered frombinary-data.Rmdusingknitr::rmarkdownon Nov 16 2024.Last update: 2023-03-13
Started: 2019-08-10
Applicability domain methods for continuous data
Rendered fromcontinuous-data.Rmdusingknitr::rmarkdownon Nov 16 2024.Last update: 2023-03-13
Started: 2019-08-10
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 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 pcas | 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_pca' | score.apd_pca |
| Score new samples using similarity methods | score.apd_similarity |
