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: <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].
| Version: |
1.2 |
| Depends: |
R (≥ 4.0) |
| Imports: |
Rcpp (≥ 1.0.5), stringr, igraph, grDevices, graphics |
| LinkingTo: |
Rcpp |
| Published: |
2022-05-12 |
| DOI: |
10.32614/CRAN.package.BCT |
| 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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=BCT
to link to this page.