Package: BCT
Type: Package
Title: Bayesian Context Trees for Discrete Time Series
Version: 1.2
Date: 2022-11-05
Author: Ioannis Papageorgiou, Valentinian Mihai Lungu, Ioannis Kontoyiannis
Maintainer: Valentinian Mihai Lungu <valentinian.mihai@gmail.com>
Description: 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]. 
License: GPL (>= 2)
LazyData: true
Encoding: UTF-8
SystemRequirements: C++11
Imports: Rcpp (>= 1.0.5), stringr, igraph, grDevices, graphics
LinkingTo: Rcpp
Depends: R (>= 4.0)
RoxygenNote: 7.1.2
NeedsCompilation: yes
Packaged: 2022-05-12 13:30:53 UTC; Valentinian
Repository: CRAN
Date/Publication: 2022-05-12 14:00:05 UTC
Built: R 4.6.0; aarch64-apple-darwin20; 2025-07-18 06:28:26 UTC; unix
Archs: BCT.so.dSYM
