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BCT  

Bayesian Context Trees for Discrete Time Series
View on CRAN: Click here


Download and install BCT package within the R console
Install from CRAN:
install.packages("BCT")

Install from Github:
library("remotes")
install_github("cran/BCT")

Install by package version:
library("remotes")
install_version("BCT", "1.2")



Attach the package and use:
library("BCT")
Maintained by
Valentinian Mihai Lungu
[Scholar Profile | Author Map]
First Published: 2020-11-23
Latest Update: 2022-05-12
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: [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' [stat.ME], March 2022].
How to cite:
Valentinian Mihai Lungu (2020). BCT: Bayesian Context Trees for Discrete Time Series. R package version 1.2, https://cran.r-project.org/web/packages/BCT. Accessed 03 May. 2025.
Previous versions and publish date:
1.0 (2020-11-23 10:20), 1.1 (2020-12-07 23:30)
Other packages that cited BCT R package
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Complete documentation for BCT
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