{ "_id": "6ac0b13191601a2ad299764f", "Package": "BCT", "Type": "Package", "Title": "Bayesian Context Trees for Discrete Time Series", "Version": "1.3", "Date": "2026-08-03", "Authors@R": "c(person(given = \"Ioannis\",\nfamily = \"Papageorgiou\",\nrole = \"aut\"),\nperson(given = c(\"Valentinian\", \"Mihai\"),\nfamily = \"Lungu\",\nrole = c(\"aut\", \"cre\"),\nemail = \"valentinian.mihai@gmail.com\"),\nperson(given = \"Ioannis\",\nfamily = \"Kontoyiannis\",\nrole = \"aut\"))", "Description": "An implementation of a collection of tools for exact\nBayesian inference with discrete times series. This package\ncontains functions that can be used for prediction, model\nselection, estimation, segmentation/change-point detection and\nother statistical tasks. Specifically, the functions provided\ncan be used for the exact computation of the prior predictive\nlikelihood of the data, for the identification of the a\nposteriori most likely (MAP) variable-memory Markov models, for\ncalculating the exact posterior probabilities and the AIC and\nBIC scores of these models, for prediction with respect to\nlog-loss and 0-1 loss and segmentation/change-point detection.\nExample data sets from finance, genetics, animal communication\nand meteorology are also provided. Detailed descriptions of the\nunderlying theory and algorithms can be found in [Kontoyiannis\net al. 'Bayesian Context Trees: Modelling and exact inference\nfor discrete time series.' Journal of the Royal Statistical\nSociety: Series B (Statistical Methodology), April 2022.\nAvailable at: [stat.ME], July\n2020] and [Lungu et al. 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