https://alfonsoiodicede.r-universe.devRecent package updates in alfonsoiodicedeR-universehttps://github.com/alfonsoiodicede.pnghttps://alfonsoiodicede.r-universe.devTue, 09 Jun 2026 21:40:08 GMTiodicede@unina.it (Alfonso Iodice DEnza)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.https://github.com/r-universe/alfonsoiodicede/actions/runs/27262787937Tue, 09 Jun 2026 21:40:08 GMTmanydist0.5.0successhttps://alfonsoiodicede.r-universe.devhttps://github.com/cran/manydist