Significance test of Spearman's Rho or Kendall's Tau 
             between short-range dependent random variables.

We present 'corto' (Correlation Tool), a simple package to infer
 gene regulatory networks and visualize master regulators from gene expression
 data using DPI (Data Processing Inequality) and bootstrapping to recover edges.
 An initial step is performed to calculate all significant
 edges between a list of source nodes (centroids) and target genes.
 Then all triplets containing two centroids and one target are tested
 in a DPI step which removes edges. A bootstrapping process then calculates
 the robustness of the network, eventually re-adding edges previously removed by DPI.
 The algorithm has been optimized to run outside a computing cluster, using a fast correlation
 implementation. The package finally provides functions to calculate network enrichment
 analysis from RNA-Seq and ATAC-Seq signatures as described in the article by
 Giorgi lab (2020) <doi:10.1093/bioinformatics/btaa223>.

Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with 'corx'.

Implements bound constrained optimal sample size allocation (BCOSSA) framework described in Bulus & Dong (2021) <doi:10.1080/00220973.2019.1636197> for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes.
  Minimum detectable effect size (MDES) and power computations for MRDDs allow polynomial functional form specification for the score variable (with or without interaction with the treatment indicator). See Bulus (2021) <doi:10.1080/19345747.2021.1947425>.

Fast fitting of Stable Isotope Mixing Models in R. Allows for the inclusion of covariates. Also has built-in summary functions and plot functions which allow for the creation of isospace plots. Variational Bayes is used to fit these models, methods as described in: Tran et al., (2021) <doi:10.48550/arXiv.2103.01327>.