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kcmeans  

Conditional Expectation Function Estimation with K-Conditional-Means
View on CRAN: Click here


Download and install kcmeans package within the R console
Install from CRAN:
install.packages("kcmeans")

Install from Github:
library("remotes")
install_github("cran/kcmeans")

Install by package version:
library("remotes")
install_version("kcmeans", "0.1.0")



Attach the package and use:
library("kcmeans")
Maintained by
Thomas Wiemann
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-11-30
Latest Update: 2023-11-30
Description:
Implementation of the KCMeans regression estimator studied by Wiemann (2023) 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) , allowing for global solutions in time polynomial in the number of observed categories.
How to cite:
Thomas Wiemann (2023). kcmeans: Conditional Expectation Function Estimation with K-Conditional-Means. R package version 0.1.0, https://cran.r-project.org/web/packages/kcmeans. Accessed 28 Jan. 2025.
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Complete documentation for kcmeans
Functions, R codes and Examples using the kcmeans R package
Some associated functions: kcmeans . predict.kcmeans . 
Some associated R codes: kcmeans.R . ols.R .  Full kcmeans package functions and examples
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