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]
(<https://orcid.org/0000-0001-7170-867X>),
Reza Mohammadi
[ctb, cph] (<https://orcid.org/0000-0001-9538-0648>),
Alex Lenkoski
[ctb, cph] (<https://orcid.org/0000-0002-6664-0292>),
Andrew Johnson [ctb] (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 |