Package: manydist 0.5.0

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.

Authors:Alfonso Iodice D'Enza [aut, cre], Angelos Markos [aut], Michel van de Velden [aut], Carlo Cavicchia [aut]