Package: BGGM 2.1.6.9000

Philippe Rast

BGGM: Bayesian Gaussian Graphical Models

Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Authors:Donald Williams [aut], Joris Mulder [aut], Philippe Rast [aut, cre]

BGGM_2.1.6.9000.tar.gz
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BGGM_2.1.6.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BGGM/json (API)

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

Bug tracker:https://github.com/rast-lab/bggm/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • asd_ocd - Data: Autism and Obssesive Compulsive Disorder
  • bfi - Data: 25 Personality items representing 5 factors
  • csws - Data: Contingencies of Self-Worth Scale
  • depression_anxiety_t1 - Data: Depression and Anxiety
  • depression_anxiety_t2 - Data: Depression and Anxiety
  • gss - Data: 1994 General Social Survey
  • ifit - Data: ifit Intensive Longitudinal Data
  • iri - Data: Interpersonal Reactivity Index
  • ptsd - Data: Post-Traumatic Stress Disorder
  • ptsd_cor1 - Data: Post-Traumatic Stress Disorder
  • ptsd_cor2 - Data: Post-Traumatic Stress Disorder
  • ptsd_cor3 - Data: Post-Traumatic Stress Disorder
  • ptsd_cor4 - Data: Post-Traumatic Stress Disorder
  • rsa - Data: Resilience Scale of Adults
  • Sachs - Data: Sachs Network
  • tas - Data: Toronto Alexithymia Scale
  • women_math - Data: Women and Mathematics

On CRAN:

Conda:

openblascpp

8.75 score 3 stars 1 packages 222 scripts 831 downloads 2 mentions 33 exports 130 dependencies

Last updated from:85aa98108b. Checks:13 OK. Indexed: yes.

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Exports:bggm_missingconfirmconstrained_posteriorconvergenceestimateexplorefisher_r_to_zfisher_z_to_rgen_netgen_ordinalggm_compare_confirmggm_compare_estimateggm_compare_exploreggm_compare_ppcimpute_datamappcor_matpcor_sumpcor_to_corplot_priorposterior_predictposterior_samplesprecisionpredictabilitypredicted_probabilityprior_belief_ggmprior_belief_varregression_summaryroll_your_ownselectvar_estimateweighted_adj_matzero_order_cors

Dependencies:abindbackportsbainBergmberryFunctionsBFpackBHbootbridgesamplingBrobdingnagcachemcallrcheckmateclicodacpp11crayonDEoptimRdescdistributionaldplyrergmevaluatefarverfastmapforcatsgenericsGGallyggplot2ggridgesggstatsgluegridExtragslgtablehighrhmsinlineisobandknitrlabelingLaplacesDemonlatticelavaanlifecyclelme4logsplineloolpSolveAPImagrittrMASSMatrixmatrixcalcMatrixModelsmatrixStatsmcmcMCMCpackmemoisemetaBMAmgcvminqamnormtmvnfastmvtnormnetworknlmenloptrnumDerivotelpatchworkpbivnormpillarpkgbuildpkgconfigplyrposteriorpracmaprettyunitsprocessxprogresspspurrrQRMquadprogquantregQuickJSRR6rbibutilsRColorBrewerRcppRcppArmadilloRcppDistRcppEigenRcppParallelRcppProgressRdpackreformulasreshapeRglpkrlangrlerobustbaserstanrstantoolsS7sandwichscalesslamsnaSparseMStanHeadersstatnet.commonstringistringrsurvivaltensorAtibbletidyrtidyselecttimeDatetimeSeriestruncnormtrustutf8vctrsviridisLitewithrxfunyamlzoo

MCMC Diagnostics
Introduction | R packages | ACF plot | Trace plot
Rendered frommcmc_diagnostics.Rmdusingknitr::rmarkdown

Last update: 2026-07-02
Started: 2020-05-21

Controlling for Variables
Introduction | Example 1: Multivariate Regression | Notes about Implementation | Correct | Fit Model | Summary | Incorrect | Example 2: Multivariate Probit | Example 3: Gaussian Copula Graphical Model | Select Graph | Note
Rendered fromcontrol.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2020-05-27

Custom Network Comparisons
Background | Basic Idea | R packages | Data | Illustrative Examples | Correlation | Step 1: Define Custom Function | Step 2: Compute the Observed Score | Step 3: Predictive Check | Hamming Distance | Partial Correlation Matrix Distance | Assortment | Expected Influence | Two Notes of Caution | Conclusion | References
Rendered fromppc_custom.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2020-05-19

Custom Network Statistics
Background | Basic Idea | R packages | Data | Fit Model | Examples | Expected Influence | Bridge Strength | Assortment | Note | References
Rendered fromnetstat_custom.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2020-05-20

Graphical VAR
Introduction | R package | Data | Estimation | Fit Model | Compare to Classical | Summarize Model | Select Graph | Plot Graph | Predictability | Explore | Confirm | Note
Rendered fromvar_model.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2020-06-04

In Tandem: Confirmatory and Exploratory Testing
External Link
Rendered fromin_tandem.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2020-05-24

Network Plots
Introduction | R packages | Estimate | Fit Model | Select Graph | Plot Graph | Customize Plot | Layout | Bayesian Hypothesis Testing | Note | References
Rendered fromnetplot.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2020-05-21

Predictability: Binary, Ordinal, and Continuous
Background | R packages | Binary | Ordinal | Continuous | Note | References
Rendered frompredictability.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2020-05-20

Testing Sums
Introduction | R package | One Group | Sum to String | Fit Model | Test Sums | Plot Results | Two Groups | Fit Models | Sanity Check | Notes
Rendered fromtest_sum.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2020-05-25

Three Ways to Test the Same Hypothesis
Introduction | R package | Data | Approach 1: Posterior Difference | Hypothesis | Fit Models | Extract the Samples | Sum and Compute Difference | Posterior Probability | Approach 2: Predictive Check | Define Function | Predictive Check | Plot | Approach 3: Bayesian Hypothesis Testing | Test Hypothesis | Plot Hypothesis | Sensitivity Analysis | Conclusion | References
Rendered fromhyp_3_ways.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2020-05-23

Readme and manuals

Help Manual

Help pageTopics
Data: Autism and Obssesive Compulsive Disorderasd_ocd
Data: 25 Personality items representing 5 factorsbfi
GGM: Missing Databggm_missing
Compute Posterior Distributions from Graph Search Resultsbma_posterior
Compute Regression Parameters for 'estimate' Objectscoef.estimate
Compute Regression Parameters for 'explore' Objectscoef.explore
GGM: Confirmatory Hypothesis Testingconfirm
Constrained Posterior Distributionconstrained_posterior
MCMC Convergenceconvergence
Data: Contingencies of Self-Worth Scale (CSWS)csws
Data: Depression and Anxiety (Time 1)depression_anxiety_t1
Data: Depression and Anxiety (Time 2)depression_anxiety_t2
GGM: Estimationestimate
GGM: Exploratory Hypothesis Testingexplore
Fisher Z Transformationfisher_r_to_z
Fisher Z Back Transformationfisher_z_to_r
Simulate a Partial Correlation Matrixgen_net
Generate Ordinal and Binary datagen_ordinal
GGM Compare: Confirmatory Hypothesis Testingggm_compare_confirm
GGM Compare: Estimateggm_compare_estimate
GGM Compare: Exploratory Hypothesis Testingggm_compare_explore
GGM Compare: Posterior Predictive Checkggm_compare_ppc
Perform Bayesian Graph Search and Optional Model Averagingggm_search
Data: 1994 General Social Surveygss
Data: ifit Intensive Longitudinal Dataifit
Obtain Imputed Datasetsimpute_data
Data: Interpersonal Reactivity Index (IRI)iri
Maximum A Posteriori Precision Matrixmap
Extract the Partial Correlation Matrixpcor_mat
Partial Correlation Sumpcor_sum
Compute Correlations from the Partial Correlationspcor_to_cor
Plot: Prior Distributionplot_prior
Plot 'confirm' objectsplot.confirm
Plot 'ggm_compare_ppc' Objectsplot.ggm_compare_ppc
Plot 'pcor_sum' Objectplot.pcor_sum
Plot 'predictability' Objectsplot.predictability
Plot 'roll_your_own' Objectsplot.roll_your_own
Network Plot for 'select' Objectsplot.select
Plot 'summary.estimate' Objectsplot.summary.estimate
Plot 'summary.explore' Objectsplot.summary.explore
Plot 'summary.ggm_compare_estimate' Objectsplot.summary.ggm_compare_estimate
Plot 'summary.ggm_compare_explore' Objectsplot.summary.ggm_compare_explore
Plot 'summary.select.explore' Objectsplot.summary.select.explore
Plot 'summary.var_estimate' Objectsplot.summary.var_estimate
Posterior Predictive Distributionposterior_predict
Extract Posterior Samplesposterior_samples
Precision Matrix Posterior Distributionprecision
Model Predictions for 'estimate' Objectspredict.estimate
Model Predictions for 'explore' Objectspredict.explore
Model Predictions for 'var_estimate' Objectspredict.var_estimate
Predictability: Bayesian Variance Explained (R2)predictability
Predicted Probabilitiespredicted_probability
Print method for 'BGGM' objectsprint.BGGM
Prior Belief Gaussian Graphical Modelprior_belief_ggm
Prior Belief Graphical VARprior_belief_var
Data: Post-Traumatic Stress Disorderptsd
Data: Post-Traumatic Stress Disorder (Sample # 1)ptsd_cor1
Data: Post-Traumatic Stress Disorder (Sample # 2)ptsd_cor2
Data: Post-Traumatic Stress Disorder (Sample # 3)ptsd_cor3
Data: Post-Traumatic Stress Disorder (Sample # 4)ptsd_cor4
Summarary Method for Multivariate or Univarate Regressionregression_summary
Compute Custom Network Statisticsroll_your_own
Data: Resilience Scale of Adults (RSA)rsa
Data: Sachs NetworkSachs
S3 'select' methodselect
Graph Selection for 'estimate' Objectsselect.estimate
Graph selection for 'explore' Objectsselect.explore
Graph Selection for 'ggm_compare_estimate' Objectsselect.ggm_compare_estimate
Graph selection for 'ggm_compare_explore' Objectsselect.ggm_compare_explore
Graph Selection for 'var.estimate' Objectselect.var_estimate
Summarize 'coef' Objectssummary.coef
Summary method for 'estimate.default' objectssummary.estimate
Summary Method for 'explore.default' Objectssummary.explore
Summary method for 'ggm_compare_estimate' objectssummary.ggm_compare_estimate
Summary Method for 'ggm_compare_explore' Objectssummary.ggm_compare_explore
Summary Method for 'predictability' Objectssummary.predictability
Summary Method for 'select.explore' Objectssummary.select.explore
Summary Method for 'var_estimate' Objectssummary.var_estimate
Data: Toronto Alexithymia Scale (TAS)tas
VAR: Estimationvar_estimate
Extract the Weighted Adjacency Matrixweighted_adj_mat
Data: Women and Mathematicswomen_math
Zero-Order Correlationszero_order_cors