Provides methods for (auto)covariance/correlation function estimation 
    in change point regression with stationary errors circumventing the pre-estimation
    of the underlying signal of the observations. Generic, first-order, (m+1)-gapped,
    difference-based autocovariance function estimator is based on M. Levine and I. Tecuapetla-Gómez (2023) <doi:10.48550/arXiv.1905.04578>. Bias-reducing, second-order, (m+1)-gapped, 
    difference-based estimator is based on I. Tecuapetla-Gómez and A. Munk (2017) 
    <doi:10.1111/sjos.12256>. Robust autocovariance estimator for change point regression with autoregressive errors is based on S. Chakar et al. (2017) <doi:10.3150/15-BEJ782>. 
    It also includes a general projection-based method for covariance matrix estimation.

Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package 'BayesTree'.

A way to apply Distance-Based Common Spatial Patterns
    (DB-CSP) techniques in different fields, both classical Common Spatial
    Patterns (CSP) as well as DB-CSP. The method is composed of two
    phases: applying the DB-CSP algorithm and performing a classification.
    The main idea behind the CSP is to use a linear transform to project
    data into low-dimensional subspace with a projection matrix, in such a
    way that each row consists of weights for signals. This transformation
    maximizes the variance of two-class signal matrices.The dbcsp object
    is created to compute the projection vectors. For exploratory and
    descriptive purpose, plot and boxplot functions can be used. Functions
    train, predict and selectQ are implemented for the classification
    step.

Tools for working with a new versatile
	discrete distribution, the db ("discretised Beta")
	distribution.  This package provides density (probability),
	distribution, inverse distribution (quantile) and random
	data generation functions for the db family.  It provides
	functions to effect conveniently maximum likelihood
	estimation of parameters, and a variety of useful plotting
	functions.  It provides goodness of fit tests and functions
	to calculate the Fisher information, different estimates of
	the hessian of the log likelihood and Monte Carlo estimation
	of the covariance matrix of the maximum likelihood parameter
	estimates.  In addition it provides analogous tools for
	working with the beta-binomial distribution which has been
	proposed as a competitor to the db distribution.