Bayesian Estimation of Change-Points in the Slope of Multivariate Time-Series R package beast version 1.2 Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2019) <doi:10.1515/ijb-2018-0052> for a detailed presentation of the method. Software Depends: R (>= 2.10) Imports: RColorBrewer Panagiotis Papastamoulis <papapast@yahoo.gr> Comprehensive R Archive Network (CRAN) Panagiotis Papastamoulis [aut, cre] (ORCID: <https://orcid.org/0000-0001-9468-7613>) GPL-2 2026-02-04 application/tgz https://CRAN.R-project.org/package=beast doi:10.32614/CRAN.package.beast