Package: mBvs 1.92

mBvs: Bayesian Variable Selection Methods for Multivariate Data

Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data (Lee et al., Biometrics, 2017 <doi:10.1111/biom.12557>) and zero-inflated count data (Lee et al., Biostatistics, 2020 <doi:10.1093/biostatistics/kxy067>).

Authors:Kyu Ha Lee, Mahlet G. Tadesse, Brent A. Coull, Jacqueline R. Starr

mBvs_1.92.tar.gz
mBvs_1.92.zip(r-4.5)mBvs_1.92.zip(r-4.4)mBvs_1.92.zip(r-4.3)
mBvs_1.92.tgz(r-4.4-x86_64)mBvs_1.92.tgz(r-4.4-arm64)mBvs_1.92.tgz(r-4.3-x86_64)mBvs_1.92.tgz(r-4.3-arm64)
mBvs_1.92.tar.gz(r-4.5-noble)mBvs_1.92.tar.gz(r-4.4-noble)
mBvs_1.92.tgz(r-4.4-emscripten)mBvs_1.92.tgz(r-4.3-emscripten)
mBvs.pdf |mBvs.html
mBvs/json (API)

# Install 'mBvs' in R:
install.packages('mBvs', repos = c('https://kleest0.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • gsl– GNU Scientific Library (GSL)
Datasets:
  • simData_cont - A simulated data set containing multivariate normal responses and continuous covariates
  • simData_mzip - A simulated data set containing multivariate zero-inflated count responses and a continuous covariate

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 4 scripts 289 downloads 4 exports 1 dependencies

Last updated 7 months agofrom:9589167444. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 13 2024
R-4.5-win-x86_64OKOct 13 2024
R-4.5-linux-x86_64OKOct 13 2024
R-4.4-win-x86_64OKOct 13 2024
R-4.4-mac-x86_64OKOct 13 2024
R-4.4-mac-aarch64OKOct 13 2024
R-4.3-win-x86_64OKOct 13 2024
R-4.3-mac-x86_64OKOct 13 2024
R-4.3-mac-aarch64OKOct 13 2024

Exports:initiate_startValuesmmzipBvsmvnBvsmzipBvs

Dependencies:mvtnorm