This tool performs pairwise correlation analysis and estimate causality.
    Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria.

Design and use of control charts for detecting mean changes 
 based on a delayed updating of the in-control parameter estimates.
 See Capizzi and Masarotto (2019) <doi:10.1080/00224065.2019.1640096> for the
 description of the method.

Provides six variants of two-way correspondence analysis (ca):
   simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca,
   singly ordered non symmetrical ca, and doubly ordered non symmetrical
   ca.

Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is
             implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates.
             An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since 
             pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids
             clustering methods. The method is Dahl, Andros, Carter (2022+) <doi:10.1002/sam.11602>.

Provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. 
  Details are in Jeong et al. (2019) <doi:10.1101/gr.245571.118> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.

Implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.

A set of functions that helps you to generate descriptive statistics based on the variable types.

Fit multiclass Classification version of Bayesian Adaptive Smoothing Splines (CBASS) to data using reversible jump MCMC. The multiclass classification problem consists of a response variable that takes on unordered categorical values with at least three levels, and a set of inputs for each response variable. The CBASS model consists of a latent multivariate probit formulation, and the means of the latent Gaussian random variables are specified using adaptive regression splines. The MCMC alternates updates of the latent Gaussian variables and the spline parameters. All the spline parameters (variables, signs, knots, number of interactions), including the number of basis functions used to model each latent mean, are inferred. Functions are provided to process inputs, initialize the chain, run the chain, and make predictions. Predictions are made on a probabilistic basis, where, for a given input, the probabilities of each categorical value are produced. See Marrs and Francom (2023) "Multiclass classification using Bayesian multivariate adaptive regression splines" Under review.