NEWS
manydist 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.