BCT: Bayesian Context Trees for Discrete Time Series

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].

Version: 1.2
Depends: R (≥ 4.0)
Imports: Rcpp (≥ 1.0.5), stringr, igraph, grDevices, graphics
LinkingTo: Rcpp
Published: 2022-05-12
Author: Ioannis Papageorgiou, Valentinian Mihai Lungu, Ioannis Kontoyiannis
Maintainer: Valentinian Mihai Lungu <valentinian.mihai at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: C++11
CRAN checks: BCT results

Documentation:

Reference manual: BCT.pdf

Downloads:

Package source: BCT_1.2.tar.gz
Windows binaries: r-devel: BCT_1.2.zip, r-release: BCT_1.2.zip, r-oldrel: BCT_1.2.zip
macOS binaries: r-release (arm64): BCT_1.2.tgz, r-oldrel (arm64): BCT_1.2.tgz, r-release (x86_64): BCT_1.2.tgz
Old sources: BCT archive

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