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kamila
View on CRAN: Click
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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", "0.2.0") Attach the package and use:
library("kamila")
Maintained by
Alexander Foss
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
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 0.2.0, https://cran.r-project.org/web/packages/kamila. Accessed 29 Oct. 2026.
Previous versions and publish date:
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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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