{ "_id": "6a1f3a20b401979e73428f96", "Package": "RoBMA", "Title": "Robust Bayesian Meta-Analyses", "Version": "4.0.0", "Maintainer": "František Bartoš ", "Authors@R": "c( \nperson(\"František\", \"Bartoš\", role = c(\"aut\", \"cre\"),\nemail = \"f.bartos96@gmail.com\", comment = c(ORCID = \"0000-0002-0018-5573\")),\nperson(\"Maximilian\", \"Maier\", role = \"aut\",\nemail = \"maximilianmaier0401@gmail.com\", comment = c(ORCID = \"0000-0002-9873-6096\")),\nperson(\"Eric-Jan\", \"Wagenmakers\", role = \"ths\",\ncomment = c(ORCID = \"0000-0003-1596-1034\")),\nperson(\"Joris\", \"Goosen\", role = \"ctb\"),\nperson(\"Matthew\", \"Denwood\", role=\"cph\",\ncomment=\"Original copyright holder of some modified code where indicated.\"),\nperson(\"Martyn\", \"Plummer\", role=\"cph\",\ncomment=\"Original copyright holder of some modified code where indicated.\")\n)", "Description": "A framework for Bayesian meta-analysis, including model\nestimation, prior specification, model comparison, prediction,\nsummaries, visualizations, and diagnostics. The package fits\nsingle and model-averaged meta-analytic, meta-regression,\nmultilevel, publication bias adjusted, and generalized linear\nmixed models The model-averaged meta-analytic models combine\ncompeting models based on their predictive performance, weight\ninference by posterior model probabilities, and test model\ncomponents using Bayes factors (e.g., effect vs. no effect;\nBartoš et al., 2022, ; Maier, Bartoš &\nWagenmakers, 2022, ; Bartoš et al.,\n2025, ). Users can specify flexible\nprior distributions for effect sizes, heterogeneity,\npublication bias (including selection models and PET-PEESE),\nand moderators.", "URL": "https://fbartos.github.io/RoBMA/", "BugReports": "https://github.com/FBartos/RoBMA/issues", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "SystemRequirements": "JAGS >= 4.3.1 (https://mcmc-jags.sourceforge.io/)", "NeedsCompilation": "yes", "RdMacros": "Rdpack", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "jags libicu-dev", "Repository": "https://fbartos.r-universe.dev", "Date/Publication": "2026-05-07 13:26:48 UTC", "RemoteUrl": "https://github.com/fbartos/robma", "RemoteRef": "HEAD", "RemoteSha": "09d07b90f91c26d4e2eac863647cb858633f8d78", "Packaged": { "Date": "2026-05-07 14:07:49 UTC", "User": "root" }, "Author": "František Bartoš [aut, cre] (ORCID:\n),\nMaximilian Maier [aut] (ORCID: ),\nEric-Jan Wagenmakers [ths] (ORCID:\n),\nJoris Goosen [ctb],\nMatthew Denwood [cph] (Original copyright holder of some modified code\nwhere indicated.),\nMartyn Plummer [cph] (Original copyright holder of some modified code\nwhere indicated.)", "MD5sum": "ecb7ec3c0e1aa6df2ba2ece57686d8f9", "_user": "fbartos", "_type": "src", "_file": "RoBMA_4.0.0.tar.gz", "_fileid": "dd4a118aff50b95a32c25a746a89b271b25c7f8b64e2e2d6327efb05798365f1", "_filesize": 6286738, "_sha256": "dd4a118aff50b95a32c25a746a89b271b25c7f8b64e2e2d6327efb05798365f1", "_created": "2026-05-07T14:07:49.000Z", "_published": "2026-06-02T20:16:32.437Z", "_distro": "noble", "_jobs": [ { "job": 79163232255, "time": 210, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "6857440174" }, { "job": 79163231977, "time": 259, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6857462357" }, { "job": 79163232346, "time": 233, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "6857449946" }, { "job": 79163231927, "time": 261, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6857460810" }, { "job": 79163231775, "time": 151, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "6857417096" }, { "job": 79163231931, "time": 378, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "6857508920" }, { "job": 79163231381, "time": 191, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "6857432440" }, { "job": 79163231568, "time": 324, "config": "macos-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6857486311" }, { "job": 79163231137, "time": 695, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "6857354074" }, { "job": 79163230686, "time": 160, "config": "wasm-release", "r": "4.6.0", "check": "FAIL", "artifact": "" }, { "job": 79163231198, "time": 217, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "6857443853" }, { "job": 79163231865, "time": 199, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "6857435838" }, { "job": 79163231872, "time": 220, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "6857446725" } ], "_buildurl": "https://github.com/r-universe/fbartos/actions/runs/25500334519", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/fbartos/robma", "_commit": { "id": "09d07b90f91c26d4e2eac863647cb858633f8d78", "author": "František Bartoš <38475991+FBartos@users.noreply.github.com>", "committer": "GitHub ", "message": "version 4.0.0\n\n### Breaking changes\n- rewrites the package around the unified `brma` class hierarchy. Single-model fits now use `brma()`, `brma.glmm()`, `bselmodel()`, `bPET()`, and `bPEESE()`; model-averaged fits use `BMA()`, `BMA.glmm()`, and `RoBMA()`.\n- removes the legacy `RoBMA.reg()`, `NoBMA()`, `NoBMA.reg()`, `BiBMA()`, and `BiBMA.reg()` constructors. Use `mods`, `scale`, and `cluster` in the new constructors, `BMA()` for no-bias normal-likelihood model averaging, and `BMA.glmm()` for GLMM model averaging.\n- replaces old input aliases such as `d`, `r`, `logOR`, `OR`, `z`, `y`, `se`, `v`, `n`, `study_names`, `study_ids`, `weight`, and `transformation` with `yi`, `vi`/`sei`, `ni`, `slab`, `cluster`, `weights`, `measure`, `output_measure`, and `transform`.\n- removes legacy helper APIs including `combine_data()`, `check_setup()`, `extract_posterior()`, `marginal_summary()`, `marginal_plot()`, `plot_models()`, `adjusted_effect()`, `as_zcurve()`, and the old z-curve plotting methods.\n- normal-likelihood fitting functions now require an explicit `measure` for fitted models. Use `measure = \"GEN\"` for generic effect sizes without a known unit-information scale.\n- `update()` for `brma` objects now focuses on extending MCMC samples, updating labels, and refreshing cached quantities, not changing model structure.\n- `set_convergence_checks()` no longer accepts the old `remove_failed` and `balance_probability` arguments.\n\n### Features\n- adds `brma()` / `brma.norm()` for single normal-likelihood Bayesian meta-analysis, including random-effects, meta-regression, multilevel, and location-scale models.\n- adds `brma.glmm()` for binomial-normal and Poisson-normal GLMM meta-analysis from raw two-arm counts (`measure = \"OR\"` and `\"IRR\"`).\n- adds single-model publication-bias constructors `bselmodel()`, `bPET()`, and `bPEESE()`.\n- adds `BMA()` / `BMA.norm()` for Bayesian model averaging without publication-bias adjustment.\n- adds `BMA.glmm()` for Bayesian model averaging of GLMM me", "time": 1778160408 }, "_maintainer": { "name": "František Bartoš", "email": "f.bartos96@gmail.com", "login": "fbartos", "bluesky": "@fbartos.bsky.social", "orcid": "0000-0002-0018-5573", "twitter": "@BartosFra", "uuid": 38475991 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.0.0", "role": "Depends" }, { "package": "mvtnorm", "role": "LinkingTo" }, { "package": "BayesTools", "version": ">= 0.3.0", "role": "Imports" }, { "package": "bridgesampling", "role": "Imports" }, { "package": "loo", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "runjags", "role": "Imports" }, { "package": "rjags", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "mvtnorm", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "Rdpack", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "coda", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "metafor", "role": "Suggests" }, { "package": "posterior", "role": "Suggests" }, { "package": "weightr", "role": "Suggests" }, { "package": "lme4", "role": "Suggests" }, { "package": "fixest", "role": "Suggests" }, { "package": "metaBMA", "role": "Suggests" }, { "package": "emmeans", "role": "Suggests" }, { "package": "metadat", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" }, { "package": "vdiffr", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "covr", "role": "Suggests" } ], "_owner": "fbartos", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-24", "n": 5 }, { "week": "2025-29", "n": 4 }, { "week": "2025-30", "n": 3 }, { "week": "2025-31", "n": 1 }, { "week": "2025-33", "n": 2 }, { "week": "2025-37", "n": 2 }, { "week": "2025-51", "n": 1 }, { "week": "2026-19", "n": 1 } ], "_tags": [ { "name": "v3.5.0", "date": "2025-06-11" }, { "name": "v3.5.1", "date": "2025-07-24" }, { "name": "v3.6.0", "date": "2025-09-10" }, { "name": "v3.6.1", "date": "2025-12-17" }, { "name": "v4.0.0", "date": "2026-05-07" } ], "_topics": [ "meta-analysis", "model-averaging", "publication-bias", "jags", "cpp" ], "_stars": 10, "_contributors": [ { "user": "fbartos", "count": 109, "uuid": 38475991 }, { "user": "jorisgoosen", "count": 6, "uuid": 29062713 }, { "user": "copilot", "count": 2, "uuid": 198982749 } ], "_userbio": { "uuid": 38475991, "type": "user", "name": "František Bartoš", "description": "A (Psych Methods) PhD Candidate at the University of Amsterdam. I’m interested in meta-analyses, publication bias, replicability, and Bayesian inference." }, "_downloads": { "count": 604, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/RoBMA" }, "_mentions": 1, "_devurl": "https://github.com/fbartos/robma", "_pkgdown": "https://fbartos.github.io/RoBMA/", "_searchresults": 71, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/RoBMA.html", "manual.pdf" ], "_homeurl": "https://github.com/fbartos/robma", "_realowner": "fbartos", "_cranurl": true, "_releases": [ { "version": "1.0.3", "date": "2020-08-06" }, { "version": "1.0.4", "date": "2020-08-07" }, { "version": "1.0.5", "date": "2020-10-13" }, { "version": "1.1.0", "date": "2020-10-30" }, { "version": "1.1.1", "date": "2020-11-10" }, { "version": "1.1.2", "date": "2020-12-10" }, { "version": "1.2.0", "date": "2021-01-21" }, { "version": "1.2.1", "date": "2021-02-16" }, { "version": "2.0.0", "date": "2021-07-08" }, { "version": "2.1.0", "date": "2021-10-13" }, { "version": "2.1.1", "date": "2021-11-03" }, { "version": "2.1.2", "date": "2022-01-12" }, { "version": "2.2.1", "date": "2022-04-06" }, { "version": "2.2.2", "date": "2022-04-20" }, { "version": "2.3.0", "date": "2022-07-12" }, { "version": "2.3.1", "date": "2022-07-17" }, { "version": "2.3.2", "date": "2023-03-13" }, { "version": "3.0.0", "date": "2023-06-01" }, { "version": "3.0.1", "date": "2023-06-02" }, { "version": "3.1.0", "date": "2023-07-18" }, { "version": "3.2.0", "date": "2024-12-12" }, { "version": "3.3.0", "date": "2025-01-17" }, { "version": "3.4.0", "date": "2025-02-04" }, { "version": "3.5.0", "date": "2025-06-11" }, { "version": "3.5.1", "date": "2025-08-01" }, { "version": "3.6.0", "date": "2025-09-10" }, { "version": "3.6.1", "date": "2025-12-17" } ], "_exports": [ "add_loo", "add_marglik", "add_waic", "as_draws", "as_draws_array", "as_draws_df", "as_draws_list", "as_draws_matrix", "as_draws_rvars", "as_zplot", "bayes_factor", "bf", "blup", "BMA", "BMA.glmm", "BMA.norm", "bPEESE", "bPET", "bridge_sampler", "brma", "brma.glmm", "brma.norm", "bselmodel", "check_loo", "contr.independent", "contr.meandif", "contr.orthonormal", "covratio", "dffits", "estimate_unit_information_sd", "funnel", "galbraith", "interpret", "logml", "loo", "loo_compare", "loo_weights", "marginal_means", "plot_diagnostic", "plot_diagnostic_autocorrelation", "plot_diagnostic_density", "plot_diagnostic_trace", "plot_pet_peese", "plot_prior", "plot_weightfunction", "pooled_effect", "pooled_heterogeneity", "post_prob", "print_prior", "prior", "prior_factor", "prior_informed", "prior_none", "prior_PEESE", "prior_PET", "prior_weightfunction", "radial", "ranef", "regplot", "RoBMA", "RoBMA.get_option", "RoBMA.options", "set_autofit_control", "set_convergence_checks", "summary_heterogeneity", "summary_models", "true_effects", "vif", "waic", "wf_cumulative", "wf_fixed", "wf_independent", "zplot" ], "_datasets": [ { "name": "Anderson2010", "title": "23 experimental studies from Anderson et al. 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(2010) that meet the best practice criteria", "topics": [ "Anderson2010" ] }, { "page": "Andrews2021", "title": "39 study rows on household chaos and child executive functions from a meta-analysis by Andrews et al. (2021)", "topics": [ "Andrews2021" ] }, { "page": "as_draws.brma", "title": "Convert brma Objects to posterior Draws Formats", "topics": [ "as_draws", "as_draws.brma", "as_draws.default", "as_draws_array", "as_draws_array.brma", "as_draws_array.default", "as_draws_df", "as_draws_df.brma", "as_draws_df.default", "as_draws_list", "as_draws_list.brma", "as_draws_list.default", "as_draws_matrix", "as_draws_matrix.brma", "as_draws_matrix.default", "as_draws_rvars", "as_draws_rvars.brma", "as_draws_rvars.default" ] }, { "page": "as_draws.brma_samples", "title": "Convert brma_samples to posterior Draws Formats", "topics": [ "as_draws.brma_samples", "as_draws_array.brma_samples", "as_draws_df.brma_samples", "as_draws_list.brma_samples", "as_draws_matrix.brma_samples", "as_draws_rvars.brma_samples" ] }, { "page": "as_zplot.brma", "title": "Transform brma Object to Zplot", "topics": [ "as_zplot", "as_zplot.brma" ] }, { "page": "as.matrix.brma_samples", "title": "Convert brma_samples to Matrix", "topics": [ "as.matrix.brma_samples" ] }, { "page": "Bem2011", "title": "9 experimental studies from Bem (2011) as described in Bem et al. (2011)", "topics": [ "Bem2011" ] }, { "page": "bf.brma", "title": "Bayes Factor for brma Objects", "topics": [ "bayes_factor.brma", "bf.brma" ] }, { "page": "blup", "title": "Best Linear Unbiased Predictions (BLUPs)", "topics": [ "blup" ] }, { "page": "blup.brma", "title": "Best Linear Unbiased Predictions for brma Objects", "topics": [ "blup.brma" ] }, { "page": "BMA", "title": "Bayesian Model-Averaged Meta-Analysis", "topics": [ "BMA", "BMA.norm" ] }, { "page": "BMA.glmm", "title": "Bayesian Model-Averaged Generalized Meta-Analysis", "topics": [ "BMA.glmm" ] }, { "page": "bPEESE", "title": "Bayesian Precision-Effect Estimate with Standard Errors (PEESE) Model", "topics": [ "bPEESE" ] }, { "page": "bPET", "title": "Bayesian Precision-Effect Test (PET) Model", "topics": [ "bPET" ] }, { "page": "bridge_sampler.brma", "title": "Bridge Sampling for brma Objects", "topics": [ "bridge_sampler.brma" ] }, { "page": "brma", "title": "Bayesian Meta-Analysis", "topics": [ "brma", "brma.norm" ] }, { "page": "brma.glmm", "title": "Bayesian Generalized Meta-Analysis", "topics": [ "brma.glmm" ] }, { "page": "bselmodel", "title": "Bayesian Selection Model", "topics": [ "bselmodel" ] }, { "page": "check_loo.brma", "title": "Check LOO Diagnostics for brma Objects", "topics": [ "check_loo", "check_loo.brma" ] }, { "page": "coef.brma", "title": "Extract Model Coefficients for brma Objects", "topics": [ "coef.brma" ] }, { "page": "contr.BayesTools", "title": "BayesTools Contrast Matrices", "topics": [ "contr.BayesTools", "contr.independent", "contr.meandif", "contr.orthonormal" ] }, { "page": "cooks.distance.brma", "title": "Cook's Distance for brma Objects", "topics": [ "cooks.distance.brma" ] }, { "page": "covratio.brma", "title": "COVRATIO for brma Objects", "topics": [ "covratio", "covratio.brma" ] }, { "page": "data_input", "title": "Input Data Specification", "topics": [ "data_input" ] }, { "page": "dfbetas.brma", "title": "DFBETAS for brma Objects", "topics": [ "dfbetas.brma" ] }, { "page": "dffits.brma", "title": "DFFITS for brma Objects", "topics": [ "dffits", "dffits.brma" ] }, { "page": "estimate_unit_information_sd", "title": "Estimate Unit Information Standard Deviation", "topics": [ "estimate_unit_information_sd" ] }, { "page": "fitted.brma", "title": "Fitted Values for brma Objects", "topics": [ "fitted.brma" ] }, { "page": "fitting_specification", "title": "Fitting specification", "topics": [ "fitting_specification" ] }, { "page": "funnel", "title": "Funnel Plot for brma Object", "topics": [ "funnel", "funnel.brma" ] }, { "page": "hatvalues.brma", "title": "Hat Values for brma Objects", "topics": [ "hatvalues.brma" ] }, { "page": "Havrankova2025", "title": "1159 effect sizes from a meta-analysis of beauty and professional success by Havránková et al. (2025)", "topics": [ "Havrankova2025" ] }, { "page": "hist.zplot_brma", "title": "Histogram of Z-Statistics", "topics": [ "hist.zplot_brma" ] }, { "page": "Hoppen2025", "title": "37 studies from a meta-analysis of social comparison as a behavior change technique by Hoppen et al. (2025)", "topics": [ "Hoppen2025" ] }, { "page": "influence.brma", "title": "Measure Influence for brma Objects", "topics": [ "influence.brma" ] }, { "page": "interpret", "title": "Interpret brma Results", "topics": [ "interpret", "interpret.brma", "interpret.default", "print.interpret.brma" ] }, { "page": "Johnides2025", "title": "412 effect sizes from a meta-analysis of secondary benefits of family-based treatments by Johnides et al. (2025)", "topics": [ "Johnides2025" ] }, { "page": "Kroupova2021", "title": "881 estimates from 69 studies of a relationship between employment and educational outcomes collected by Kroupova et al. (2021)", "topics": [ "Kroupova2021" ] }, { "page": "lines.zplot_brma", "title": "Add Zplot Density Lines", "topics": [ "lines.zplot_brma" ] }, { "page": "logLik.brma", "title": "Extract Log-Likelihood Matrix from brma Object", "topics": [ "logLik.brma" ] }, { "page": "logml.brma", "title": "Log Marginal Likelihood for brma Objects", "topics": [ "logml.brma" ] }, { "page": "loo_compare.brma", "title": "Compare brma Models Using LOO", "topics": [ "loo_compare", "loo_compare.brma" ] }, { "page": "loo_compare.loo", "title": "Compare loo Objects Using LOO", "topics": [ "loo_compare.loo" ] }, { "page": "loo_weights.brma", "title": "Extract Normalized PSIS Weights from brma Object", "topics": [ "loo_weights", "loo_weights.brma" ] }, { "page": "loo.brma", "title": "LOO-PSIS for brma Objects", "topics": [ "loo", "loo.brma" ] }, { "page": "Lui2015", "title": "18 studies of a relationship between acculturation mismatch and intergenerational cultural conflict collected by Lui (2015)", "topics": [ "Lui2015" ] }, { "page": "ManyLabs16", "title": "55 effect sizes from Many Labs 2 replication studies of Tversky and Kahneman (1981) framing effects", "topics": [ "ManyLabs16" ] }, { "page": "marginal_means", "title": "Estimated Marginal Means", "topics": [ "marginal_means" ] }, { "page": "marginal_means.brma", "title": "Estimated Marginal Means for brma Objects", "topics": [ "marginal_means.brma" ] }, { "page": "nobs.brma", "title": "Number of Observations for brma Objects", "topics": [ "nobs.brma" ] }, { "page": "plot_diagnostic", "title": "Plot MCMC Diagnostics", "topics": [ "plot_diagnostic", "plot_diagnostic.brma", "plot_diagnostic_autocorrelation", "plot_diagnostic_autocorrelation.brma", "plot_diagnostic_density", "plot_diagnostic_density.brma", "plot_diagnostic_trace", "plot_diagnostic_trace.brma" ] }, { "page": "plot_pet_peese", "title": "Plot PET-PEESE Fit of brma Object", "topics": [ "plot_pet_peese", "plot_pet_peese.brma" ] }, { "page": "plot_prior", "title": "Plot Prior Distributions", "topics": [ "plot_prior", "plot_prior.brma", "plot_prior.prior" ] }, { "page": "plot_weightfunction", "title": "Plots Weight Function of brma Object", "topics": [ "plot_weightfunction", "plot_weightfunction.brma" ] }, { "page": "plot.brma", "title": "Plots brma Object", "topics": [ "plot.brma" ] }, { "page": "plot.marginal_means.brma", "title": "Plot Estimated Marginal Means", "topics": [ "plot.marginal_means.brma" ] }, { "page": "plot.zplot_brma", "title": "Plot Zplot Results", "topics": [ "plot.zplot_brma" ] }, { "page": "pooled_effect", "title": "Pooled Effect Size", "topics": [ "pooled_effect" ] }, { "page": "pooled_effect.brma", "title": "Pooled Effect Size for brma Objects", "topics": [ "pooled_effect.brma" ] }, { "page": "pooled_heterogeneity", "title": "Pooled Heterogeneity", "topics": [ "pooled_heterogeneity" ] }, { "page": "pooled_heterogeneity.brma", "title": "Pooled Heterogeneity for brma Objects", "topics": [ "pooled_heterogeneity.brma" ] }, { "page": "post_prob.brma", "title": "Posterior Model Probabilities for brma Objects", "topics": [ "post_prob.brma" ] }, { "page": "Poulsen2006", "title": "5 studies with a tactile outcome assessment from Poulsen et al. (2006) of the effect of potassium-containing toothpaste on dentine hypersensitivity", "topics": [ "Poulsen2006" ] }, { "page": "predict.brma", "title": "Predict From brma Object", "topics": [ "predict.brma" ] }, { "page": "print_prior", "title": "Print Prior Distributions", "topics": [ "print_prior", "print_prior.brma", "print_prior.prior" ] }, { "page": "print.brma_samples", "title": "Print brma_samples Object", "topics": [ "print.brma_samples" ] }, { "page": "print.marginal_means.brma", "title": "Print Estimated Marginal Means", "topics": [ "print.marginal_means.brma" ] }, { "page": "print.RoBMA_data", "title": "Print method for RoBMA_data objects", "topics": [ "print.RoBMA_data" ] }, { "page": "print.summary_heterogeneity.brma", "title": "Print Summary of Heterogeneity", "topics": [ "print.summary_heterogeneity.brma" ] }, { "page": "print.summary.brma_samples", "title": "Print summary.brma_samples Object", "topics": [ "print.summary.brma_samples" ] }, { "page": "print.summary.marginal_means.brma", "title": "Print Summary of Estimated Marginal Means", "topics": [ "print.summary.marginal_means.brma" ] }, { "page": "print.summary.zplot_brma", "title": "Print Zplot Summary", "topics": [ "print.summary.zplot_brma" ] }, { "page": "print.vif.brma", "title": "Print VIF Results", "topics": [ "print.vif.brma" ] }, { "page": "prior", "title": "Prior Distribution", "topics": [ "prior" ] }, { "page": "prior_factor", "title": "Factor Prior", "topics": [ "prior_factor" ] }, { "page": "prior_informed", "title": "Informed Prior", "topics": [ "prior_informed" ] }, { "page": "prior_none", "title": "Empty Prior", "topics": [ "prior_none" ] }, { "page": "prior_PEESE", "title": "PEESE Prior", "topics": [ "prior_PEESE" ] }, { "page": "prior_PET", "title": "PET Prior", "topics": [ "prior_PET" ] }, { "page": "prior_specification", "title": "Prior specification", "topics": [ "prior_specification" ] }, { "page": "prior_weightfunction", "title": "Weightfunction Prior", "topics": [ "prior_weightfunction", "wf_cumulative", "wf_fixed", "wf_independent" ] }, { "page": "publication_bias_prior_specification", "title": "Publication-bias prior specification", "topics": [ "bias_prior_specification", "publication_bias_prior_specification" ] }, { "page": "qqnorm.brma", "title": "Normal QQ Plot for brma Object", "topics": [ "qqnorm.brma" ] }, { "page": "radial", "title": "Radial (Galbraith) Plot for brma Object", "topics": [ "galbraith", "galbraith.brma", "radial", "radial.brma" ] }, { "page": "ranef", "title": "Random Effects", "topics": [ "ranef" ] }, { "page": "ranef.brma", "title": "Random Effects for brma Objects", "topics": [ "ranef.brma" ] }, { "page": "regplot", "title": "Regression Plot (Bubble Plot) for brma Object", "topics": [ "regplot", "regplot.brma" ] }, { "page": "residuals.brma", "title": "Residuals for brma Objects", "topics": [ "residuals.brma" ] }, { "page": "RoBMA", "title": "Robust Bayesian Model-Averaged Meta-Analysis", "topics": [ "RoBMA" ] }, { "page": "RoBMA_control", "title": "Control MCMC fitting process", "topics": [ "RoBMA_control", "set_autofit_control", "set_convergence_checks" ] }, { "page": "RoBMA_options", "title": "Options for the RoBMA package", "topics": [ "RoBMA.get_option", "RoBMA.options", "RoBMA_options" ] }, { "page": "RoBMA_prior_specification", "title": "Prior specification for model-averaging", "topics": [ "RoBMA_prior_specification" ] }, { "page": "rstandard.brma", "title": "Internally Standardized Residuals for brma Objects", "topics": [ "rstandard.brma" ] }, { "page": "rstudent.brma", "title": "Externally Standardized (Studentized) Residuals for brma Objects", "topics": [ "rstudent.brma" ] }, { "page": "summary_heterogeneity", "title": "Summary of Heterogeneity", "topics": [ "summary_heterogeneity" ] }, { "page": "summary_heterogeneity.brma", "title": "Summary of Heterogeneity for brma Objects", "topics": [ "summary_heterogeneity.brma" ] }, { "page": "summary_models", "title": "Summarize Model-Averaged Component Weights", "topics": [ "print.summary_models.RoBMA", "summary_models", "summary_models.RoBMA" ] }, { "page": "summary.brma", "title": "Summarize brma Object", "topics": [ "print.brma", "print.summary.brma", "summary.brma" ] }, { "page": "summary.brma_samples", "title": "Summarize brma_samples Object", "topics": [ "summary.brma_samples" ] }, { "page": "summary.marginal_means.brma", "title": "Summarize Estimated Marginal Means", "topics": [ "summary.marginal_means.brma" ] }, { "page": "summary.zplot_brma", "title": "Summarize Zplot Results", "topics": [ "summary.zplot_brma" ] }, { "page": "true_effects", "title": "True Effects", "topics": [ "true_effects" ] }, { "page": "true_effects.brma", "title": "True Effects for brma Objects", "topics": [ "true_effects.brma" ] }, { "page": "update.brma", "title": "Update a brma Fit", "topics": [ "update.brma" ] }, { "page": "vif", "title": "Variance Inflation Factors", "topics": [ "vif" ] }, { "page": "vif.brma", "title": 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