Package: kamila 0.1.2

kamila: Methods for Clustering Mixed-Type Data

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) <doi:10.1007/s10994-016-5575-7> and Foss & Markatou (2018) <doi:10.18637/jss.v083.i13>.

Authors:Alexander Foss [aut, cre], Marianthi Markatou [aut]

kamila_0.1.2.tar.gz
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kamila.pdf |kamila.html
kamila/json (API)

# Install 'kamila' in R:
install.packages('kamila', repos = c('https://ahfoss.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/ahfoss/kamila/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

4.20 score 16 stars 20 scripts 172 downloads 7 exports 5 dependencies

Last updated 1 years agofrom:af50489d10. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-win-x86_64OKNov 04 2024
R-4.5-linux-x86_64OKNov 04 2024
R-4.4-win-x86_64OKNov 04 2024
R-4.4-mac-x86_64OKNov 04 2024
R-4.4-mac-aarch64OKNov 04 2024
R-4.3-win-x86_64OKNov 04 2024
R-4.3-mac-x86_64OKNov 04 2024
R-4.3-mac-aarch64OKNov 04 2024

Exports:classifyKamiladptmCppdummyCodeFactorDfgenMixedDatagmsClustkamilawkmeans

Dependencies:abindgtoolsKernSmoothplyrRcpp