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.

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: <arXiv:2007.14900> [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' <arXiv:2203.04341> [stat.ME], March 2022].

Methods for choosing the rank of an SVD (singular value decomposition) 
    approximation via cross validation. The package provides both Gabriel-style 
    "block" holdouts and Wold-style "speckled" holdouts. It also includes an 
    implementation of the SVDImpute algorithm. For more information about
    Bi-cross-validation, see Owen & Perry's 2009 AoAS article
    (at <arXiv:0908.2062>) and Perry's 2009 PhD thesis
    (at <arXiv:0909.3052>).