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dbrobust (version 1.0.0)

Robust Distance-Based Visualization and Analysis of Mixed-Type Data

Description

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) .

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Version

Install

install.packages('dbrobust')

Monthly Downloads

100

Version

1.0.0

License

GPL-3

Maintainer

Eva

Last Published

September 22nd, 2025

Functions in dbrobust (1.0.0)

robust_covariance_gv

Robust Covariance Estimation Based on Geometric Variability

robust_distances

Compute Robust Squared Distances for Mixed Data

Visualize a Distance or Similarity Matrix as a Heatmap with Clustering

visualize_distances

Visualize Distance Matrices via MDS, Heatmap, or Network Graph

Plot MDS Results with Grouped Scatter and Density Plots (Internal)

Plot a Network Graph from a Distance Matrix

Compute Robust Generalized Gower Distance

Data_MC_contamination

Moderate-correlation dataset with contamination

Data_HC_no_contamination

High-correlation dataset without contamination

calculate_distances

Compute Distance or Similarity Matrices

Data_HC_contamination

High-correlation dataset with contamination

Force a Pairwise Squared Distance Matrix to Euclidean Form

Compute pairwise binary distances

dist_categorical

Compute pairwise distances for categorical data

Compute Gower dissimilarity for mixed-type data

get_custom_palette

Generate a Custom Color Palette

dist_continuous

Compute pairwise distances for continuous numeric data

convert_to_dist

Convert a similarity or distance matrix to a 'dist' object

Data_MC_no_contamination

Moderate-correlation dataset without contamination

Format distance or similarity matrix output

Robust RelMS Distance