Package: MMVBVS 0.8.0
MMVBVS: Missing Multivariate Bayesian Variable Selection
A variable selection tool for multivariate normal variables with missing-at-random values using Bayesian Hierarchical Model. Visualization functions show the posterior distribution of gamma (inclusion variables) and beta (coefficients). Users can also visualize the heatmap of the posterior mean of covariance matrix. Kim, T. Nicolae, D. (2019) <https://github.com/tk382/MMVBVS/blob/master/workingpaper.pdf>. Guan, Y. Stephens, M. (2011) <doi:10.1214/11-AOAS455>.
Authors:
MMVBVS_0.8.0.tar.gz
MMVBVS_0.8.0.tar.gz(r-4.5-noble)MMVBVS_0.8.0.tar.gz(r-4.4-noble)
MMVBVS_0.8.0.tgz(r-4.4-emscripten)MMVBVS_0.8.0.tgz(r-4.3-emscripten)
MMVBVS.pdf |MMVBVS.html✨
MMVBVS/json (API)
# Install 'MMVBVS' in R: |
install.packages('MMVBVS', repos = 'https://cloud.r-project.org') |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:a014847175. Checks:3 OK. Indexed: no.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 10 2025 |
R-4.5-linux-x86_64 | OK | Mar 10 2025 |
R-4.4-linux-x86_64 | OK | Mar 10 2025 |
Exports:beta_distmmvbvsplot_betaplot_gammaplot_sigma
Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshapereshape2rlangscalesstringistringrtibbleutf8vctrsviridisLitewithr
Citation
To cite package ‘MMVBVS’ in publications use:
Kim T (2019). MMVBVS: Missing Multivariate Bayesian Variable Selection. R package version 0.8.0, https://CRAN.R-project.org/package=MMVBVS.
ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.
Corresponding BibTeX entry:
@Manual{, title = {MMVBVS: Missing Multivariate Bayesian Variable Selection}, author = {Tae Kim}, year = {2019}, note = {R package version 0.8.0}, url = {https://CRAN.R-project.org/package=MMVBVS}, }
Readme and manuals
MMVBVS
Multivariate Bayesian Selection algorithm, applied to multi-tissue gene expression level data.
Details in this workflowr vignette.
Working paper in here.