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kamila  

Methods for Clustering Mixed-Type Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("kamila", "0.1.2")



Attach the package and use:
library("kamila")
Maintained by
Alexander Foss
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All associated links for this package
First Published: 2016-08-19
Latest Update: 2020-03-13
Description:
Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) and Foss & Markatou (2018) .
How to cite:
Alexander Foss (2016). kamila: Methods for Clustering Mixed-Type Data. R package version 0.1.2, https://cran.r-project.org/web/packages/kamila. Accessed 06 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:51), 0.1.1.1 (2016-08-19 00:46), 0.1.1.2 (2018-02-18 05:41), 0.1.1.3 (2019-03-16 20:50)
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Complete documentation for kamila
Functions, R codes and Examples using the kamila R package
Some associated functions: classifyKamila . dptmCpp . dummyCodeFactorDf . genMixedData . gmsClust . kamila-package . kamila . wkmeans . 
Some associated R codes: RcppExports.R . calc_approx_bic_deprec.R . gen_mixed_data.R . kamila.R . misc_functions.R . modha_spangler.R . prediction_strength_deprec.R .  Full kamila package functions and examples
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