Package: vbmp 1.75.0

Nicola Lama

vbmp: Variational Bayesian Multinomial Probit Regression

Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination.

Authors:Nicola Lama <[email protected]>, Mark Girolami <[email protected]>

vbmp_1.75.0.tar.gz
vbmp_1.75.0.zip(r-4.5)vbmp_1.75.0.zip(r-4.4)vbmp_1.75.0.zip(r-4.3)
vbmp_1.75.0.tgz(r-4.5-any)vbmp_1.75.0.tgz(r-4.4-any)vbmp_1.75.0.tgz(r-4.3-any)
vbmp_1.75.0.tar.gz(r-4.5-noble)vbmp_1.75.0.tar.gz(r-4.4-noble)
vbmp_1.75.0.tgz(r-4.4-emscripten)vbmp_1.75.0.tgz(r-4.3-emscripten)
vbmp.pdf |vbmp.html✨
vbmp/json (API)

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

Peer review:

Datasets:

On BioConductor:vbmp-1.75.0(bioc 3.21)vbmp-1.74.0(bioc 3.20)

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

classification

3.30 score 4 scripts 365 downloads 7 exports 0 dependencies

Last updated 3 months agofrom:8c41b839d6. Checks:1 OK, 7 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 29 2025
R-4.5-winNOTEJan 29 2025
R-4.5-macNOTEJan 29 2025
R-4.5-linuxNOTEJan 29 2025
R-4.4-winNOTEJan 29 2025
R-4.4-macNOTEJan 29 2025
R-4.3-winNOTEJan 29 2025
R-4.3-macNOTEJan 29 2025

Exports:covParamsplotDiagnosticspredClasspredErrorpredictCPPpredLikvbmp

Dependencies:

vbmp Tutorial

Rendered fromvbmp.Rnwusingutils::Sweaveon Jan 29 2025.

Last update: 2013-11-01
Started: 2013-11-01