Changes in version 0.5.0 (2026-06-09)                  

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.