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kcmeans: Conditional Expectation Function Estimation withK-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
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

Documentation:

Reference manual:kcmeans.html ,kcmeans.pdf
Vignettes:Get Started (source)

Downloads:

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 dependencies:

Reverse imports:civ

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=kcmeansto link to this page.


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