Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. For further details, see: Alexander C. Murph et al. (2023) <doi:10.48550/arXiv.2310.02940>.
Version: 0.1.3 Depends: R (≥ 3.5.0) Imports: Rcpp (≥ 1.0.7), parallel (≥ 3.6.2), Matrix, Hotelling, CholWishart, ggplot2, gridExtra (≥ 0.9.1), BDgraph, methods, MASS, stats, ess LinkingTo: Rcpp, RcppArmadillo, RcppEigen, Matrix, CholWishart, BH Published: 2024-01-27 DOI: 10.32614/CRAN.package.bayesWatch Author: Alexander C. Murph [aut, cre], Reza Mohammadi [ctb, cph], Alex Lenkoski [ctb, cph], Andrew Johnson [ctb] (email: andrew.johnson@arjohnsonau.com) Maintainer: Alexander C. Murph <murph290 at gmail.com> License: GPL-3 Copyright: file COPYRIGHTS NeedsCompilation: yes Citation: bayesWatch citation info Materials: README NEWS CRAN checks: bayesWatch results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=bayesWatch to link to this page.
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