dispersionIndicators0.1.5 package

Indicators for the Analysis of Dispersion of Datasets with Batched and Ordered Samples

calculate_convex_hull

Calculate Convex Hulls for one variable

calculate_convex_indicators

Calculate the intra/inter batch dispersion indicators and their ratio ...

compute_ratio

Calculate the intra/inter batch dispersion ratio indicator on convex h...

compute_shoelace_core

Compute the shoelace core for convex hulls of a single variable

compute_icm_distances

Compute ICM (Integrated Covariance Mahalanobis) Distances

compute_individual_batch

Computes Integrated Covariance Mahalanobis (ICM) distances of all indi...

compute_individual_global

Computes Integrated Covariance Mahalanobis (ICM) distances of all indi...

compute_individual

Computes Integrated Covariance Mahalanobis (ICM) distances for individ...

compute_inter_batch_dispersion

Calculate the inter batch dispersion indicator on convex hulls of a si...

compute_inter

Computes Integrated Covariance Mahalanobis (ICM) distances between bat...

compute_intra_batch_dispersion

Calculate the intra batch dispersion indicator on convex hulls of a si...

compute_intra

Computes Integrated Covariance Mahalanobis (ICM) mean distances within...

convex_analysis_of_variables

Analyze a set of variables using convex hulls.

hull_data_list_check

Function to check if hull_data_list is a valid list of data frames

plot_all_convex_hulls

Plot all convex hulls for each variable in a PDF file.

plot_convex_hull

Plot the convex hulls of a single variable.

save_icm_distances_csv

Save ICM Distances to CSV Files

single_variable_df_check

Function to check if a single variable data frame is valid

Provides methods for analyzing the dispersion of tabular datasets with batched and ordered samples. Based on convex hull or integrated covariance Mahalanobis, several indicators are implemented for inter and intra batch dispersion analysis. It is designed to facilitate robust statistical assessment of data variability, supporting applications in exploratory data analysis and quality control, for such datasets as the one found in metabololomics studies. For more details see Salanon (2024) <doi:10.1016/j.chemolab.2024.105148> and Salanon (2025) <doi:10.1101/2025.08.01.668073>.