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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 |
| 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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