kcmeans: Conditional Expectation Function Estimation with K-Conditional-Means

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
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

Documentation:

Reference manual: kcmeans.pdf
Vignettes: Get Started

Downloads:

Package source: kcmeans_0.1.0.tar.gz
Windows binaries: r-prerel: kcmeans_0.1.0.zip, r-release: kcmeans_0.1.0.zip, r-oldrel: kcmeans_0.1.0.zip
macOS binaries: r-prerel (arm64): kcmeans_0.1.0.tgz, r-release (arm64): kcmeans_0.1.0.tgz, r-oldrel (arm64): kcmeans_0.1.0.tgz, r-prerel (x86_64): kcmeans_0.1.0.tgz, r-release (x86_64): kcmeans_0.1.0.tgz

Reverse dependencies:

Reverse imports: civ

Linking:

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