Package: c2c 0.1.0

c2c: Compare Two Classifications or Clustering Solutions of Varying Structure

Compare two classifications or clustering solutions that may or may not have the same number of classes, and that might have hard or soft (fuzzy, probabilistic) membership. Calculate various metrics to assess how the clusters compare to each other. The calculations are simple, but provide a handy tool for users unfamiliar with matrix multiplication. This package is not geared towards traditional accuracy assessment for classification/ mapping applications - the motivating use case is for comparing a probabilistic clustering solution to a set of reference or existing class labels that could have any number of classes (that is, without having to degrade the probabilistic clustering to hard classes).

Authors:Mitchell Lyons [aut, cre]

c2c_0.1.0.tar.gz
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c2c.pdf |c2c.html
c2c/json (API)
NEWS

# Install 'c2c' in R:
install.packages('c2c', repos = c('https://mitchest.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mitchest/c2c/issues

On CRAN:

3.70 score 7 scripts 179 downloads 9 exports 0 dependencies

Last updated 7 years agofrom:5d21ae74c2. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-winOKNov 07 2024
R-4.5-linuxOKNov 07 2024
R-4.4-winOKNov 07 2024
R-4.4-macOKNov 07 2024
R-4.3-winOKNov 07 2024
R-4.3-macOKNov 07 2024

Exports:calculate_clustering_metricsclass_entropyclass_purityget_conf_matget_hardlabels_to_matrixoverall_entropyoverall_puritypercentage_agreement

Dependencies:

c2c workflow

Rendered fromc2c-workflow.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2017-07-21
Started: 2017-07-21