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.

Statistical summary of STRUCTURE output. STRUCTURE is a K-means clustering method for inferring population structure and assigning individuals to populations using genetic data. Pritchard JK, Stephens M, Donnelly PJ (2000) <DOI:10.1093/genetics/155.2.945>. <https://web.stanford.edu/group/pritchardlab/structure.html>.

After using this, a publication-ready correlation table with p-values
  indicated will be created.  The input can be a full data frame; any
  string and Boolean terms will be dropped as part of functionality.  Correlations and
  p-values are calculated using the 'Hmisc' framework.  Output of the 
  correlation_matrix() function is a table of strings; this gets saved out to a '.csv2'
  with the save_correlation_matrix() function for easy insertion into a paper.
  For more details about the process, consult
  <https://paulvanderlaken.com/2020/07/28/publication-ready-correlation-matrix-significance-r/>.