manydist: Distance-Based Learning for Mixed-Type Data
Provides tools for constructing, computing, and using distance
measures for numerical, categorical, and mixed-type data. The package
implements a flexible framework in which continuous and categorical
components can be combined under additive, commensurable, and
association-aware specifications. Supported methods include classical
distances such as Gower, Euclidean, Manhattan, and Mahalanobis-type
distances; categorical dissimilarities such as simple matching,
occurrence-frequency, and association-based measures; and mixed-type
presets designed to reduce biases due to variable type, scale,
distribution, redundancy, and number of categories. The package also
provides scaling options, supervised and unsupervised distance
constructions, leave-one-variable-out tools for distance-based variable
importance, and integration with distance-based learning workflows such
as nearest-neighbour prediction, partitioning around medoids, and
spectral clustering. Methods are motivated by van de Velden,
Iodice D'Enza, Markos, and Cavicchia (2026)
<doi:10.1080/10618600.2026.2680181> and related work on categorical
and mixed-type dissimilarities.
| Version: |
0.5.0 |
| Depends: |
R (≥ 4.5.0) |
| Imports: |
aricode, cluster, clusterGeneration, data.table, dials, distances, dplyr, entropy, fastDummies, forcats, fpc, generics, ggplot2, kdml, magrittr, Matrix, parsnip, philentropy, purrr, readr, recipes, Rfast, rlang, rsample, stats, tibble, tidyr, tidyselect, tune |
| Suggests: |
arules, clustMixType, FD, klaR, mclust, palmerpenguins, parallelDist, StatMatch, workflows |
| Published: |
2026-06-09 |
| DOI: |
10.32614/CRAN.package.manydist |
| Author: |
Alfonso Iodice D'Enza [aut, cre],
Angelos Markos [aut],
Michel van de Velden [aut],
Carlo Cavicchia [aut] |
| Maintainer: |
Alfonso Iodice D'Enza <iodicede at unina.it> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Citation: |
manydist citation info |
| Materials: |
NEWS |
| CRAN checks: |
manydist results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=manydist
to link to this page.