Package: bcp 4.0.4

Kaiguang Zhao

bcp: Bayesian Analysis of Change Point Problems

Provides an implementation of the Barry and Hartigan (1993) product partition model for the normal errors change point problem using Markov Chain Monte Carlo. It also extends the methodology to regression models on a connected graph (Wang and Emerson, 2015); this allows 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, Chandra Erdman, John W. Emerson, and Kaiguang Zhao

bcp_4.0.4.tar.gz
bcp_4.0.4.zip(r-4.7)bcp_4.0.4.zip(r-4.6)bcp_4.0.4.zip(r-4.5)
bcp_4.0.4.tgz(r-4.6-x86_64)bcp_4.0.4.tgz(r-4.6-arm64)bcp_4.0.4.tgz(r-4.5-x86_64)bcp_4.0.4.tgz(r-4.5-arm64)
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://zhaokg.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

4.70 score 167 scripts 243 downloads 6 mentions 4 exports 2 dependencies

Last updated from:6c41c60c51. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK152
linux-devel-x86_64OK155
source / vignettesOK201
linux-release-arm64OK146
linux-release-x86_64OK137
macos-release-arm64OK173
macos-release-x86_64OK259
macos-oldrel-arm64OK180
macos-oldrel-x86_64OK369
windows-develOK180
windows-releaseOK157
windows-oldrelOK141
wasm-releaseOK118

Exports:bcpinterval.problegacyplotmakeAdjGrid

Dependencies:RcppRcppArmadillo