Package: bgms 0.2.0.0

Maarten Marsman

bgms: Bayesian Analysis of Graphical Models

Bayesian estimation and edge selection for graphical models of mixed binary, ordinal, and continuous variables. The variable types determine the model: an ordinal Markov random field for discrete data, a Gaussian graphical model for continuous data, or a mixed Markov random field combining both. Edge inclusion is determined through spike-and-slab priors, yielding posterior inclusion probabilities for each edge. Supports multi-group comparison via 'bgmCompare()', simulation, prediction, and missing data imputation.

Authors:Maarten Marsman [aut, cre], Don van den Bergh [aut], Nikola Sekulovski [ctb], Giuseppe Arena [ctb], Laura Groot [ctb], Gali Geller [ctb]

bgms_0.2.0.0.tar.gz
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bgms_0.2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bgms/json (API)

# Install 'bgms' in R:
install.packages('bgms', repos = c('https://bayesian-graphical-modelling-lab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bayesian-graphical-modelling-lab/bgms/issues

Pkgdown/docs site:https://bayesian-graphical-modelling-lab.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • ADHD - ADHD Symptom Checklist for Children Aged 6-8 Years
  • Boredom - Short Boredom Proneness Scale Responses
  • Wenchuan - PTSD Symptoms in Wenchuan Earthquake Survivors Who Lost a Child

On CRAN:

Conda:

bayesian-inferencebayesian-statisticsedge-selectiongaussian-graphical-modelgraphical-modelshamiltonian-monte-carlomarkov-chain-monte-carlomarkov-random-fieldmcmcmetropolis-hastingsnetwork-analysisparallel-computingspike-and-slabopenblascpp

8.77 score 8 stars 1 packages 82 scripts 352 downloads 29 exports 12 dependencies

Last updated from:9f85b8a8f2. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK396
linux-devel-x86_64OK382
source / vignettesOK498
linux-release-arm64OK398
linux-release-x86_64OK381
macos-release-arm64OK218
macos-release-x86_64OK685
macos-oldrel-arm64OK348
macos-oldrel-x86_64OK581
windows-develOK580
windows-releaseOK568
windows-oldrelOK588
wasm-releaseOK321

Exports:bernoulli_priorbeta_bernoulli_priorbeta_prime_priorbgmbgmComparecauchy_priorexponential_priorextract_argumentsextract_category_thresholdsextract_edge_indicatorsextract_essextract_group_paramsextract_indicator_priorsextract_indicatorsextract_log_oddsextract_main_effectsextract_pairwise_interactionsextract_pairwise_thresholdsextract_partial_correlationsextract_posterior_inclusion_probabilitiesextract_precisionextract_rhatextract_sbmgamma_priormrfSamplernormal_priorsample_ggm_priorsbm_priorsimulate_mrf

Dependencies:BHclidqrnglifecyclerbibutilsRcppRcppArmadilloRcppParallelRdpackrlangS7sitmo

Diagnostics and Spike-and-Slab Summaries
Introduction | Example fit | Convergence diagnostics | Two ESS measures for edge-selected parameters | Traceplots | Spike-and-slab summaries | Bayes factors | NUTS diagnostics | E-BFMI | Divergent transitions | Tree depth | Non-reversible steps | Warmup and equilibration | Next steps
Rendered fromdiagnostics.Rmdusingknitr::rmarkdown

Last update: 2026-06-03
Started: 2025-09-23

Getting Started with bgms
Introduction | Wenchuan dataset | Fitting a model | Posterior summaries | Network plot | Continuous data (GGM) | Next steps
Rendered fromintro.Rmdusingknitr::rmarkdown

Last update: 2026-06-03
Started: 2025-09-23

Model Comparison with bgmCompare
Introduction | ADHD dataset | Fitting a model | Posterior summaries | Visualizing group networks | Next steps
Rendered fromcomparison.Rmdusingknitr::rmarkdown

Last update: 2026-03-04
Started: 2025-09-23

Readme and manuals

Help Manual

Help pageTopics
ADHD Symptom Checklist for Children Aged 6-8 YearsADHD
Bernoulli Prior for Inclusion Indicatorsbernoulli_prior
Beta-Bernoulli Prior for Inclusion Indicatorsbeta_bernoulli_prior
Beta-Prime Prior for Model Parametersbeta_prime_prior
Bayesian Estimation or Edge Selection for Markov Random Fieldsbgm
Bayesian Estimation and Variable Selection for Group Differences in Markov Random FieldsbgmCompare
Short Boredom Proneness Scale ResponsesBoredom
Cauchy Prior for Model Parameterscauchy_prior
Extract Coefficients from a bgmCompare Objectcoef.bgmCompare
Extract Coefficients from a bgms Objectcoef.bgms
Exponential Prior for Scale Parametersexponential_prior
Extract Model Argumentsextract_arguments
Extract Category Threshold Estimatesextract_category_thresholds
Extract Effective Sample Sizeextract_ess
Extract Group-Specific Parametersextract_group_params
Extract Indicator Prior Structureextract_indicator_priors
Extract Indicator Samplesextract_indicators
Extract Posterior Mean Log-Odds (Pairwise Interactions)extract_log_odds
Extract Main Effect Estimatesextract_main_effects
Extract Pairwise Interaction Samplesextract_pairwise_interactions
Extract Posterior Mean Partial Correlationsextract_partial_correlations
Extract Posterior Inclusion Probabilitiesextract_posterior_inclusion_probabilities
Extract Posterior Mean Precision Matrixextract_precision
Extract R-hat Convergence Diagnosticsextract_rhat
Extract Stochastic Block Model Summariesextract_sbm
Gamma Prior for Scale Parametersgamma_prior
Normal Prior for Model Parametersnormal_prior
Predict Conditional Probabilities from a Fitted bgmCompare Modelpredict.bgmCompare
Predict Conditional Probabilities from a Fitted bgms Modelpredict.bgms
Print method for 'bgmCompare' objectsprint.bgmCompare
Print method for 'bgms' objectsprint.bgms
Sample from the GGM (Partial-Association) Priorsample_ggm_prior
Stochastic Block Model Prior for Inclusion Indicatorssbm_prior
Simulate Observations from a Markov Random Fieldsimulate_mrf
Simulate Data from a Fitted bgmCompare Modelsimulate.bgmCompare
Simulate Data from a Fitted bgms Modelsimulate.bgms
Summary method for 'bgmCompare' objectssummary.bgmCompare
Summary method for 'bgms' objectssummary.bgms
PTSD Symptoms in Wenchuan Earthquake Survivors Who Lost a ChildWenchuan