# Distance-Based Learning for Mixed-Type Data

## Documentation for package ‘manydist’ version 0.5.1

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

all_dist_method_specs | Create a grid of 'mdist()' method specifications
autoplot.MDistBenchmark | Plot pairwise distance benchmark comparisons
benchmark_comparisons | Extract pairwise distance benchmark comparisons
benchmark_mdist | Benchmark and compare multiple 'mdist()' specifications
compare_lovo_mdist | Compare LOVO diagnostics across multiple distance specifications
congruence_coeff | Congruence coefficient between two configurations
dist_methods_tbl | List available 'manydist' methods
dist_methods_tbl_cat | List available categorical dissimilarities
fifa_nl | FIFA 21 Player Data - Dutch League
generate_dataset | generate toy mixed datasets for the supplementary material: figures 1 to 3
gen_mixed | Generate mixed-type clustering data with signal and noise
lovo_mdist | Leave-one-variable-out diagnostics for distance-based variable importance
lovo_method_spec | Create a LOVO method specification for 'compare_lovo_mdist()'
make_mdist_recipe | Build a recipe that computes mdist-based distance features
mdist | Mixed-type dissimilarities for distance-based learning
mdist_summary_impl | Internal: summary method implementation for MDist R6 objects
pam_dist | PAM clustering specification based on manydist dissimilarities
response_aware_methods | List response-aware methods
spectral_dist | Spectral clustering specification based on manydist dissimilarities
spectral_from_dist | Spectral clustering from a distance matrix
step_mdist | Add manydist dissimilarities to a recipe
wdi_2022 | Selected World Development Indicators for 2022