OBSOLETE: while the package still functions, a new, better, faster version is available at https://github.com/davis-berlind/MICH It covers the variance change point detection, but one has also other type of changes (mean, mean and variance, multivariate/high-dimensional setting)

MICH implements:

1. Cappello, L., Madrid Padilla, O. H. (2025), Bayesian variance change point detection with credible sets. IEEE Transations of Pattern Analysis and Machine Intelligence.
2. Berlind, D., Cappello, L., Madrid Padilla, O. H. (2025), A Bayesian framework for change-point detection with uncertainty quantification, arXiv.

for PRISCA

1. Install the package devtools

2. Load devtools using library(devtools).

3. Install prisca using

   1. install_github("lorenzocapp/prisca"), or

   2. install_github("lorenzocapp/prisca", build_vignettes = TRUE) if you want some illustrative vignettes (note: using build_vignettes = TRUE will make the install take longer).

1. intro_prisca: A short tutorial to describe the basics functioning of the package.

1. Cappello, L., Madrid Padilla, O. H. (2025), Bayesian variance change point detection with credible sets. IEEE Transations of Pattern Analysis and Machine Intelligence.