Robust Distance-Based Visualization and Analysis of Mixed-Type Data
Compute Distance or Similarity Matrices
Convert a similarity or distance matrix to a 'dist' object
Compute pairwise binary distances
Compute pairwise distances for categorical data
Compute pairwise distances for continuous numeric data
Compute Gower dissimilarity for mixed-type data
Format distance or similarity matrix output
Generate a Custom Color Palette
Force a Pairwise Squared Distance Matrix to Euclidean Form
Visualize a Distance or Similarity Matrix as a Heatmap with Clustering
Plot MDS Results with Grouped Scatter and Density Plots (Internal)
Plot a Network Graph from a Distance Matrix
Robust Covariance Estimation Based on Geometric Variability
Compute Robust Squared Distances for Mixed Data
Compute Robust Generalized Gower Distance
Robust RelMS Distance
Visualize Distance Matrices via MDS, Heatmap, or Network Graph
Robust distance-based methods applied to matrices and data frames, producing distance matrices that can be used as input for various visualization techniques such as graphs, heatmaps, or multidimensional scaling configurations. See Boj and Grané (2024) <doi:10.1016/j.seps.2024.101992>.