Implementation of the KCMeans regression estimator studied by Wiemann (2023) <doi:10.48550/arXiv.2311.17021> for expectation function estimation conditional on categorical variables. Computation leverages the unconditional KMeans implementation in one dimension using dynamic programming algorithm of Wang and Song (2011) <doi:10.32614/RJ-2011-015>, allowing for global solutions in time polynomial in the number of observed categories.
Version: | 0.1.0 |
Depends: | R (≥ 3.6) |
Imports: | stats, Ckmeans.1d.dp, MASS, Matrix |
Suggests: | testthat (≥ 3.0.0), covr, knitr, rmarkdown |
Published: | 2023-11-30 |
DOI: | 10.32614/CRAN.package.kcmeans |
Author: | Thomas Wiemann [aut, cre] |
Maintainer: | Thomas Wiemann <wiemann at uchicago.edu> |
BugReports: | https://github.com/thomaswiemann/kcmeans/issues |
License: | GPL (≥ 3) |
URL: | https://github.com/thomaswiemann/kcmeans |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | kcmeans results |
Reference manual: | kcmeans.pdf |
Vignettes: |
Get Started |
Package source: | kcmeans_0.1.0.tar.gz |
Windows binaries: | r-devel: kcmeans_0.1.0.zip, r-release: kcmeans_0.1.0.zip, r-oldrel: kcmeans_0.1.0.zip |
macOS binaries: | r-release (arm64): kcmeans_0.1.0.tgz, r-oldrel (arm64): kcmeans_0.1.0.tgz, r-release (x86_64): kcmeans_0.1.0.tgz, r-oldrel (x86_64): kcmeans_0.1.0.tgz |
Reverse imports: | civ |
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