chemmodlab: A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models

Contains a set of methods for fitting models and methods for validating the resulting models. The statistical methodologies comprise a comprehensive collection of approaches whose validity and utility have been accepted by experts in the Cheminformatics field. As promising new methodologies emerge from the statistical and data-mining communities, they will be incorporated into the laboratory. These methods are aimed at discovering quantitative structure-activity relationships (QSARs). However, the user can directly input their own choices of descriptors and responses, so the capability for comparing models is effectively unlimited.

Version: 2.0.0
Depends: R (≥ 3.6.0)
Imports: KernSmooth, MSQC, class (≥ 7.3.14), e1071 (≥ 1.6.7), elasticnet (≥ 1.1), lars (≥ 1.2), MASS (≥ 7.3.45), nnet (≥ 7.3.12), pROC (≥ 1.8), randomForest (≥ 4.6.12), rpart (≥ 4.1.10), tree (≥ 1.0.37), pls (≥ 2.5.0), caret (≥ 6.0-71), stats, graphics, grDevices, utils, methods
Suggests: knitr, rmarkdown, testthat, vdiffr (≥ 0.3.0)
Published: 2022-05-01
Author: Jacqueline Hughes-Oliver [aut], Jeremy Ash [aut, cre], Atina Brooks [aut]
Maintainer: Jeremy Ash <jrash at>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: chemmodlab results


Reference manual: chemmodlab.pdf


Package source: chemmodlab_2.0.0.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): chemmodlab_2.0.0.tgz, r-release (arm64): chemmodlab_2.0.0.tgz, r-oldrel (arm64): chemmodlab_2.0.0.tgz, r-prerel (x86_64): chemmodlab_2.0.0.tgz, r-release (x86_64): chemmodlab_2.0.0.tgz
Old sources: chemmodlab archive


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