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-x86_64)bcp_4.0.4.zip(r-4.7-arm64)bcp_4.0.4.zip(r-4.6-x86_64)bcp_4.0.4.zip(r-4.6-arm64)bcp_4.0.4.zip(r-4.5-x86_64)
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✨
DESCRIPTION
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

4.72 score 176 scripts 873 downloads 6 mentions 4 exports 2 dependencies

Last updated from:6c41c60c51. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK181
linux-devel-x86_64OK158
source / vignettesOK204
linux-release-arm64OK176
linux-release-x86_64OK142
macos-release-arm64OK104
macos-release-x86_64OK210
macos-oldrel-arm64OK102
macos-oldrel-x86_64OK274
windows-devel-arm64OK384
windows-devel-x86_64OK176
windows-release-arm64OK365
windows-release-x86_64OK163
windows-oldrel-x86_64OK114
wasm-releaseOK135

Exports:bcpinterval.problegacyplotmakeAdjGrid

Dependencies:RcppRcppArmadillo