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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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