Package: bcp 4.0.4

Kaiguang Zhao

bcp: Bayesian Analysis of Change Point Problems

Provides an implementation of the product partition model described in Barry and Hartigan (2019) <doi:10.2307/2290726> for the normal errors change point problem using Markov Chain Monte Carlo (MCMC). It also extends the methodology to regression models on a connected graph as reported in Wang and Emerson (2015) <doi:10.48550/arXiv.1509.00817>, allowing estimation of change point models with multivariate responses. Parallel MCMC, previously available in 'bcp' v.3.0.0, is currently not implemented.

Authors:Xiaofei Wang [aut], Chandra Erdman [aut], John W. Emerson [aut], Kaiguang Zhao [aut, cre]

bcp_4.0.4.tar.gz
bcp_4.0.4.tar.gz(r-4.7-arm64)bcp_4.0.4.tar.gz(r-4.7-x86_64)bcp_4.0.4.tar.gz(r-4.6-arm64)bcp_4.0.4.tar.gz(r-4.6-x86_64)
bcp_4.0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
bcp/json (API)

# Install 'bcp' in R:
install.packages('bcp', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/zhaokg/bcp/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

openblascpp

4.88 score 5 stars 167 scripts 166 downloads 6 mentions 4 exports 2 dependencies

Last updated from:fef27f979a. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK152
linux-devel-x86_64OK153
source / vignettesOK213
linux-release-arm64OK146
linux-release-x86_64OK163
wasm-releaseOK146

Exports:bcpinterval.problegacyplotmakeAdjGrid

Dependencies:RcppRcppArmadillo