The bias-corrected estimation methods for the receiver operating characteristics
  ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR)
  assumption.

Search and access more than ten thousand datasets included in 'BCRPDATA' (see <https://estadisticas.bcrp.gob.pe/estadisticas/series/ayuda/bcrpdata> for more information).

Bindings to the 'blowfish' password hashing algorithm
    <https://www.openbsd.org/papers/bcrypt-paper.pdf> derived from
    the 'OpenBSD' implementation.

A collection of tools for regression analysis of non-negative data,
    including strictly positive and zero-inflated observations, based on the class of the Box-Cox
    symmetric (BCS) distributions and its zero-adjusted extension. The BCS distributions are a 
    class of flexible probability models capable of describing different levels of skewness and 
    tail-heaviness. The package offers a comprehensive regression modeling framework, including 
    estimation and tools for evaluating goodness-of-fit.

An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: <doi:10.48550/arXiv.2007.14900> [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' <doi:10.48550/arXiv.2203.04341> [stat.ME], March 2022].