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manydist
View on CRAN: Click
here
Download and install manydist package within the R console
Install from CRAN:
install.packages("manydist")
Install from Github:
library("remotes")
install_github("cran/manydist") Install by package version:
library("remotes")
install_version("manydist", "0.5.1") Attach the package and use:
library("manydist")
Maintained by
Angelos Markos
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First Published: 2025-02-12
Latest Update: 2025-07-18
Description:
A comprehensive framework for calculating unbiased distances in datasets containing mixed-type variables (numerical and categorical). The package implements a general formulation that ensures multivariate additivity and commensurability, meaning that variables contribute equally to the overall distance regardless of their type, scale, or distribution. Supports multiple distance measures including Gower's distance, Euclidean distance, Manhattan distance, and various categorical variable distances such as simple matching, Eskin, occurrence frequency, and association-based distances. Provides tools for variable scaling (standard deviation, range, robust range, and principal component scaling), and handles both independent and association-based category dissimilarities. Implements methods to correct for biases that typically arise from different variable types, distributions, and number of categories. Particularly useful for cluster analysis, data visualization, and other distance-based methods when working with mixed data. Methods based on van de Velden et al. (2024) <doi:10.48550/arXiv.2411.00429> "Unbiased mixed variables distance".
How to cite:
Angelos Markos (2025). manydist: Distance-Based Learning for Mixed-Type Data. R package version 0.5.1, https://cran.r-project.org/web/packages/manydist. Accessed 28 Oct. 2026.
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