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/>.

Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) <DOI:10.1007/978-981-10-3307-0_4>.

An investigative tool designed to help users visualize correlations between variables in their datasets. This package aims to provide an easy and effective way to explore and visualize these correlations, making it easier to interpret and communicate results.

Set of methods to constrain numerical series and time series within
             arbitrary boundaries.