Perform additional multiple testing procedure methods to p.adjust(), 
  such as weighted Hochberg (Tamhane, A. C., & Liu, L., 2008) <doi:10.1093/biomet/asn018>, 
  ICC adjusted Bonferroni method (Shi, Q., Pavey, E. S., & Carter, R. E., 2012)
  <doi:10.1002/pst.1514> and a new correlation corrected weighted Hochberg for correlated 
  endpoints.

Performs Correlated Meta-Analysis ('corrmeta') across multiple OMIC
        scans, accounting for hidden non-independencies between elements of the
        scans due to overlapping samples, related samples, or other information. 
        For more information about the method, refer to the paper
        Province MA. (2013) <doi:10.1142/9789814447973_0023>.

In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.

Provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.

A tool for exploring correlations.  It makes it possible to
    easily perform routine tasks when exploring correlation matrices such
    as ignoring the diagonal, focusing on the correlations of certain
    variables against others, or rearranging and visualizing the matrix in
    terms of the strength of the correlations.

A clustered random forest algorithm for fitting random forests for data of independent clusters, that exhibit within cluster dependence. 
    Details of the method can be found in Young and Buehlmann (2025) <doi:10.48550/arXiv.2503.12634>.

Provides functions to extract low-correlation variable subsets using exact graph-theoretic algorithms (e.g., Eppstein–Löffler–Strash, Bron–Kerbosch) as well as greedy and spectral heuristics. Supports both numeric and mixed-type data using generalized association measures.