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Simple, fast, and automatic encodings for category data using a data.table backend. Most of the methods are an implementation of "Sufficient Representation for Categorical Variables" by Johannemann, Hadad, Athey, Wager (2019) <doi:10.48550/arXiv.1908.09874>, particularly their mean, sparse principal component analysis, low rank representation, and multinomial logit encodings.
Version: | 1.4.3 |
Imports: | glmnet, sparsepca, data.table |
Suggests: | testthat (≥ 2.1.0), covr |
Published: | 2020-03-02 |
DOI: | 10.32614/CRAN.package.categoryEncodings |
Author: | Juraj Szitas [aut, cre] |
Maintainer: | Juraj Szitas <szitas.juraj13 at gmail.com> |
BugReports: | https://github.com/JSzitas/categoryEncodings/issues |
License: | GPL-3 |
URL: | https://github.com/JSzitas/categoryEncodings |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | categoryEncodings results |
Reference manual: | categoryEncodings.pdf |
Package source: | categoryEncodings_1.4.3.tar.gz |
Windows binaries: | r-devel: categoryEncodings_1.4.3.zip, r-release: categoryEncodings_1.4.3.zip, r-oldrel: categoryEncodings_1.4.3.zip |
macOS binaries: | r-release (arm64): categoryEncodings_1.4.3.tgz, r-oldrel (arm64): categoryEncodings_1.4.3.tgz, r-release (x86_64): categoryEncodings_1.4.3.tgz, r-oldrel (x86_64): categoryEncodings_1.4.3.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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