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.

There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in  6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation.  ST5() and ST6() used two different ways to combine ST3() and ST4().  We highly recommend ST5() according to the article titled ''New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs'' published in Scientific Reports.  Please see the reference: Yu et al. (2019) <doi:10.1038/s41598-019-40167-8>.

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

An agent runtime that gives Large Language Models (LLMs)
    from 'Anthropic' <https://www.anthropic.com/>, 'OpenAI'
    <https://openai.com/>, 'Moonshot' <https://www.moonshot.ai/>, and
    'Ollama' <https://ollama.com/> direct access to a live R session with
    managed workspace state. Tools execute as R function calls with
    provenance tracking, and a deterministic retrieval system keeps
    relevant objects in context across turns. Three entry points: a
    shell command-line interface (CLI), a console read-eval-print-loop
    via chat(), and a Model Context Protocol (MCP) server via serve()
    for external clients.

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>.