A comprehensive set of functions designed for multivariate mean monitoring using the Critical-to-X Control Chart. These functions enable the determination of optimal control limits based on a specified in-control Average Run Length (ARL), the calculation of out-of-control ARL for a given control limit, and post-signal analysis to identify the specific variable responsible for a detected shift in the mean. This suite of tools provides robust support for precise and effective process monitoring and analysis.

Access chemical, hazard, bioactivity, and exposure data from the 
   Computational Toxicology and Exposure ('CTX') APIs 
   <https://www.epa.gov/comptox-tools/computational-toxicology-and-exposure-apis>. 
   'ctxR' was developed to streamline the process of accessing the information 
   available through the 'CTX' APIs without requiring prior knowledge of how to 
   use APIs. Most data is also available on the CompTox Chemical Dashboard 
   ('CCD') <https://comptox.epa.gov/dashboard/> and other resources found at the 
   EPA Computational Toxicology and Exposure Online Resources 
   <https://www.epa.gov/comptox-tools>.

Interacting with binary files can be difficult because R's types
    are a subset of what is generally supported by 'C'.  This package provides a 
    suite of functions for reading and writing binary data (with files, connections, 
    and raw vectors) using 'C' type descriptions.  These functions convert data
    between 'C' types and R types while checking for values outside the 
    type limits, 'NA' values, etc.

For ordinal rating data, estimate and test models within the family of CUB models and their extensions (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions); Simulation routines, plotting facilities and fitting measures are also provided.

A suite of functions for rapid and flexible analysis of codon 
    usage bias. It provides in-depth analysis at the codon level, including 
    relative synonymous codon usage (RSCU), tRNA weight calculations, machine 
    learning predictions for optimal or preferred codons, and visualization of 
    codon-anticodon pairing. Additionally, it can calculate various gene-
    specific codon indices such as codon adaptation index (CAI), effective 
    number of codons (ENC), fraction of optimal codons (Fop), tRNA adaptation 
    index (tAI), mean codon stabilization coefficients (CSCg), and GC contents 
    (GC/GC3s/GC4d). It also supports both standard and non-standard genetic 
    code tables found in NCBI, as well as custom genetic code tables.