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.