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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.