Package: beam 2.0.4

beam: Fast Bayesian Inference in Large Gaussian Graphical Models

Fast Bayesian inference of marginal and conditional independence structures from high-dimensional data. Leday and Richardson (2019), Biometrics, <doi:10.1111/biom.13064>.

Authors:Gwenael G.R. Leday [cre, aut], Ilaria Speranza [aut], Harry Gray [ctb]

beam_2.0.4.tar.gz
beam_2.0.4.zip(r-4.7)beam_2.0.4.zip(r-4.6)beam_2.0.4.zip(r-4.5)
beam_2.0.4.tgz(r-4.6-x86_64)beam_2.0.4.tgz(r-4.6-arm64)beam_2.0.4.tgz(r-4.5-x86_64)beam_2.0.4.tgz(r-4.5-arm64)
beam_2.0.4.tar.gz(r-4.7-arm64)beam_2.0.4.tar.gz(r-4.7-x86_64)beam_2.0.4.tar.gz(r-4.6-arm64)beam_2.0.4.tar.gz(r-4.6-x86_64)
beam_2.0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
beam/json (API)

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

Bug tracker:https://github.com/gleday/beam/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

bayesian-inferencecovariance-matrix-estimationempirical-bayesgraphical-modelshigh-dimensional-inferencemachine-learningnetwork-analysisprecision-matrix-estimationstatisticsopenblascpp

3.45 score 19 scripts 348 downloads 17 exports 21 dependencies

Last updated from:31e6e2faa7. Checks:11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE168
linux-devel-x86_64NOTE175
source / vignettesOK207
linux-release-arm64NOTE183
linux-release-x86_64NOTE179
macos-release-arm64NOTE142
macos-release-x86_64NOTE287
macos-oldrel-arm64NOTE144
macos-oldrel-x86_64NOTE280
windows-develNOTE172
windows-releaseNOTE124
windows-oldrelNOTE155
wasm-releaseOK163

Exports:beambeam.selectbgraphcondlightbeammargmcorpcorplotAdjplotCorplotMLpostExpOmegapostExpSigmaprintshowsummaryugraph

Dependencies:assertthatBHclicpp11evaluatefdrtoolgluehighrigraphknitrlatticelifecyclemagrittrMatrixpkgconfigRcppRcppArmadillorlangvctrsxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Fast Bayesian Inference in Large Gaussian Graphical Modelsbeam-package
Bayesian inference in large Gaussian graphical modelsbeam
Class beambeam-class bgraph bgraph,beam-method cond cond,beam-method marg marg,beam-method mcor mcor,beam-method pcor pcor,beam-method plotCor plotCor,beam-method plotML plotML,beam-method postExpOmega postExpOmega,beam-method postExpSigma postExpSigma,beam-method print,beam-method show,beam-method summary,beam-method ugraph ugraph,beam-method
Edge selection with multiple testing and error controlbeam.select
Class beam.selectbeam.select-class bgraph,beam.select-method cond,beam.select-method marg,beam.select-method mcor,beam.select-method pcor,beam.select-method plotAdj plotAdj,beam.select-method plotML,beam.select-method print,beam.select-method show,beam.select-method summary,beam.select-method ugraph,beam.select-method
Fast inference of a conditional independence graphlightbeam
Protein expression data.TCPAprad