manydist 0.5.1

Benchmarking and diagnostics

• Extended benchmark_mdist() to compare every pair of successful distance specifications using mean absolute distance differences, symmetric relative distance, multidimensional-scaling congruence, and alienation.
• Added optional clustering comparisons to benchmark_mdist(). Supplying cluster_k computes pairwise adjusted Rand indices for PAM, hierarchical, and/or spectral clustering; clustering is skipped when cluster_k = NULL.
• Added benchmark_comparisons() to extract the pairwise diagnostics stored in an MDistBenchmark result without recomputing the distances.
• Added an autoplot() method for MDistBenchmark objects, with annotated heatmaps for distance, geometry, and clustering-agreement diagnostics.

Distance construction and recipe workflows

• Updated step_mdist() so response-aware specifications can obtain a single outcome directly from the recipe formula during preparation. The fitted response-aware profiles are reused when new data are baked, so assessment and test outcomes are neither required nor used.
• Added the response_used argument to step_mdist(), allowing response use to be disabled explicitly.
• Allowed method_num to override the default standardization of the "euclidean" preset for numerical-only data. In particular, method_num = "none" computes ordinary Euclidean distances on the original variables.

Data

• Added wdi_2022, a documented snapshot of selected 2022 World Development Indicators for reproducible mixed-type distance examples.

Documentation and testing

• Added focused tests for response-aware step_mdist() workflows and the pairwise benchmarking interface.
• Expanded the package website with task-oriented articles on distance construction, diagnostics and benchmarking, clustering, and nearest-neighbour workflows.

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.