manydist 0.5.0

Major changes

• Expanded manydist from a package focused on mixed-type distance construction to a broader framework for distance-based learning with mixed-type data.
• Updated the package title and description to reflect support for distance construction, distance-based modelling workflows, variable-importance diagnostics, and clustering.
• Changed the package maintainer from Angelos Markos to Alfonso Iodice D’Enza.

Distance construction

• Added a revised mdist() interface and documentation for mixed-type distance construction.
• Added support for additional mixed-type distance specifications and presets.
• Added response-aware distance construction tools for supervised mixed-type workflows.
• Added interaction-aware distance components for continuous-categorical relationships.
• Added helper infrastructure for preprocessing and applying mixed-type distance specifications consistently across training and new data.
• Added utilities for generating and benchmarking distance-method specifications.

Distance-based learning workflows

• Added step_mdist() for integrating manydist distances into recipes and tidymodels workflows.
• Added nearest_neighbor_dist() and related prediction functions for nearest-neighbour models based on precomputed or manydist-generated distances.
• Added pam_dist() for partitioning around medoids using manydist dissimilarities.
• Added spectral_dist() and spectral_from_dist() for spectral clustering from distance matrices.
• Added support functions for converting distances to affinities and fitting distance-based clustering models.

Variable importance and diagnostics

• Added lovo_mdist() for leave-one-variable-out diagnostics of distance matrices.
• Added compare_lovo_mdist() and lovo_method_spec() for comparing LOVO diagnostics across multiple distance specifications.
• Added congruence- and alienation-based diagnostics for comparing multidimensional scaling configurations.
• Added optional clustering-based LOVO diagnostics using PAM, hierarchical clustering, and spectral clustering.

Data generation and benchmarking

• Added gen_mixed() and generate_dataset() for generating mixed-type example and simulation data.
• Added benchmark_mdist() for benchmarking distance specifications across datasets and method grids.
• Added all_dist_method_specs() and distance-method metadata helpers.

Documentation

• Added documentation for the new modelling, clustering, LOVO, benchmarking, and recipe functions.
• Updated the package-level description and metadata for the 0.5.0 release.
• Removed CRAN-inappropriate development files, caches, and vignette outputs from the source build.