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beast
View on CRAN: Click
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Download and install beast package within the R console
Install from CRAN:
install.packages("beast")
Install from Github:
library("remotes")
install_github("cran/beast")
Install by package version:
library("remotes")
install_version("beast", "1.1")
Attach the package and use:
library("beast")
Maintained by
Panagiotis Papastamoulis
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-11-29
Latest Update: 2018-03-16
Description:
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 (2017) for a detailed presentation of the method.
How to cite:
Panagiotis Papastamoulis (2017). beast: Bayesian Estimation of Change-Points in the Slope of Multivariate Time-Series. R package version 1.1, https://cran.r-project.org/web/packages/beast
Previous versions and publish date:
1.0 (2017-11-29 20:11)
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imports, suggests or enhances
Functions, R codes and Examples using
the beast R package
Some associated functions: FungalGrowthDataset . beast-package . beast . birthProbs . complexityPrior . computeEmpiricalPriorParameters . computePosteriorParameters . computePosteriorParametersFree . localProposal . logLikelihoodFullModel . logPrior . mcmcSampler . myUnicodeCharacters . normalizeTime0 . plot.beastject . print.beastject . proposeTheta . simMultiIndNormInvGamma . simulateFromPrior . singleLocalProposal . truncatedPoisson . updateNumberOfCutpoints .
Some associated R codes: fullMCMC_complexityPrior.R . Full beast package functions and examples
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