Distance-Based Learning for Mixed-Type Data R package manydist version 0.5.1 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. Software 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, testthat (>= 3.0.0), workflows, workflowsets Alfonso Iodice D'Enza <iodicede@unina.it> Comprehensive R Archive Network (CRAN) Alfonso Iodice D'Enza [aut, cre], Angelos Markos [aut], Michel van de Velden [aut], Carlo Cavicchia [aut] GPL-3 2026-07-23 application/tgz https://CRAN.R-project.org/package=manydist doi:10.32614/CRAN.package.manydist