cmahalanobis: Calculate Distance Measures for DataFrames

It provides functions that calculate Mahalanobis distance, Euclidean distance, Manhattan distance, Chebyshev distance, Hamming distance, Canberra distance, Minkowski dissimilarity (distance defined for p >= 1), Cosine dissimilarity, Bhattacharyya dissimilarity, Jaccard distance, Hellinger distance, Bray-Curtis dissimilarity, Sorensen-Dice dissimilarity between each pair of species in a list of data frames. These statistics are fundamental in various fields, such as cluster analysis, classification, and other applications of machine learning and data mining, where assessing similarity or dissimilarity between data is crucial. The package is designed to be flexible and easily integrated into data analysis workflows, providing reliable tools for evaluating distances in multidimensional contexts.

Version:	1.0.0
Imports:	stats, ggplot2, reshape2, gridExtra, matrixStats
Suggests:	rmarkdown, testthat (≥ 3.0.0)
Published:	2025-09-14
DOI:	10.32614/CRAN.package.cmahalanobis
Author:	Flavio Gioia [aut, cre]
Maintainer:	Flavio Gioia <flaviogioia.fg at gmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	cmahalanobis results

Documentation:

Reference manual:

cmahalanobis.html , cmahalanobis.pdf

Downloads:

Package source:	cmahalanobis_1.0.0.tar.gz
Windows binaries:	r-devel: cmahalanobis_0.5.0.zip, r-release: cmahalanobis_0.5.0.zip, r-oldrel: cmahalanobis_0.5.0.zip
macOS binaries:	r-release (arm64): cmahalanobis_1.0.0.tgz, r-oldrel (arm64): cmahalanobis_1.0.0.tgz, r-release (x86_64): cmahalanobis_1.0.0.tgz, r-oldrel (x86_64): cmahalanobis_1.0.0.tgz
Old sources:	cmahalanobis archive

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