RoBMA 4.0.0

Breaking changes

• 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().
• 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.
• 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.
• 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.
• 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.
• update() for brma objects now focuses on extending MCMC samples, updating labels, and refreshing cached quantities, not changing model structure.
• set_convergence_checks() no longer accepts the old remove_failed and balance_probability arguments.

Features

• adds brma() / brma.norm() for single normal-likelihood Bayesian meta-analysis, including random-effects, meta-regression, multilevel, and location-scale models.
• adds brma.glmm() for binomial-normal and Poisson-normal GLMM meta-analysis from raw two-arm counts (measure = "OR" and "IRR").
• adds single-model publication-bias constructors bselmodel(), bPET(), and bPEESE().
• adds BMA() / BMA.norm() for Bayesian model averaging without publication-bias adjustment.
• adds BMA.glmm() for Bayesian model averaging of GLMM meta-analyses without publication-bias adjustment.
• rewrites RoBMA() as a product-space model-averaged ensemble over effect, heterogeneity, moderator, scale, and publication-bias components.
• adds formula/data-frame input handling for effect sizes, moderators, scale predictors, clusters, labels, subsets, likelihood weights, and raw GLMM counts.
• adds default prior construction from standardized effect-size measures, estimated or manually supplied unit-information standard deviations, and informed empirical priors.
• adds prior_weightfunction(), wf_cumulative(), wf_fixed(), and wf_independent() for BayesTools-backed selection-weightfunction priors.
• adds prior_PET(), prior_PEESE(), prior_none(), prior_factor(), prior_informed(), and BayesTools contrast helpers as package-level prior utilities.
• adds posterior package interfaces via as_draws(), as_draws_array(), as_draws_df(), as_draws_list(), as_draws_matrix(), and as_draws_rvars() for fitted models and brma_samples.
• adds the brma_samples posterior-sample class with print, summary, matrix, and posterior conversion methods.
• adds predict.brma() for posterior predictions of fixed terms, cluster effects, latent true effects, observed responses, and scale terms, with newdata, conditional, bias_adjusted, output_measure, and transform support.
• adds convenience wrappers fitted(), pooled_effect(), pooled_heterogeneity(), blup(), true_effects(), and ranef() for brma objects.
• adds model-comparison helpers add_loo(), loo(), loo_compare(), loo_weights(), check_loo(), add_waic(), waic(), and logLik() using the loo package.
• adds bridge-sampling marginal likelihood support for single-model brma fits via add_marglik(), bridge_sampler(), logml(), bf(), bayes_factor(), and post_prob().
• adds residual and influence diagnostics: residuals(), rstandard(), rstudent() / LOO-PIT, hatvalues(), influence(), dfbetas(), dffits(), cooks.distance(), covratio(), and vif().
• adds plotting methods for brma objects: posterior/prior plots, funnel(), regplot(), qqnorm(), radial() / galbraith(), MCMC diagnostic plots, weightfunction plots, and PET-PEESE plots.
• adds marginal_means() with summary and plotting methods for moderator models.
• adds summary_models() for marginal and individual model-weight summaries of product-space RoBMA, BMA, and BMA.glmm objects.
• adds interpret() for concise textual interpretation of fitted brma and model-averaged objects.
• renames the zplot diagnostic API to as_zplot() and adds the direct plotting wrapper zplot(), with plot(), hist(), lines(), summary(), and print methods for zplot objects.
• adds RoBMA.options() and RoBMA.get_option() package options for defaults such as core count, automatic LOO/WAIC/marginal-likelihood computation, prior scaling defaults, and selection-bias defaults.

Changes

• renames the multilevel clustering argument to cluster.
• renames study labels to slab, matching metafor naming.
• renames likelihood weights to weights and applies them consistently to posterior fitting, log-likelihoods, LOO, WAIC, and diagnostics.
• uses measure, output_measure, and transform for effect-size scale handling. Supported conversions include SMD, COR, ZCOR, and OR; transform = "EXP" exponentiates log ratio measures for display.
• standardizes continuous predictors by default and transforms reported coefficients back to the original scale unless standardized coefficients are requested.
• uses treatment contrasts by default for single-model constructors and mean-difference contrasts by default for model-averaged constructors.
• changes predict.brma() default to type = "terms". GLMM type = "response" predictions return continuity-corrected effect-size estimators by default via as_measure = TRUE.
• separates output unit from conditioning_depth for residuals, fitted values, LOO, WAIC, and related diagnostics.
• supports estimate-level and, for multilevel models, cluster-level LOO/WAIC targets with target metadata to prevent invalid comparisons.
• keeps bridge-sampling marginal likelihoods for single-model brma objects; product-space RoBMA, BMA, and BMA.glmm objects relly on product-space only.
• routes selection-weightfunction priors through the BayesTools selection backend and selected-normal kernel, removing legacy weighted-normal mapping paths.
• uses bias_indicator and branch-aware selected-normal contexts for RoBMA publication-bias mixtures instead of inferring selection branches from omega.
• increases zplot default posterior thinning controls to 10000 samples and accepts Inf where full posterior evaluation is requested.
• adds max_samples controls to expensive funnel, regplot, and zplot summaries.
• updates the package startup message to point users to vignette("v00-introduction", package = "RoBMA").
• requires BayesTools 0.3.0 for forward API and selection-backend support.
• adds bridgesampling, loo, MASS, and parallel as imports and posterior as a suggested package.

Fixes

• fixes loading and runtime checks for the RoBMA JAGS module and native R routines.

Performance and internals

• moves fitting to JAGS product-space models with mixture-prior indicators for model averaging.
• replaces legacy weighted-normal and multivariate-normal native code with selected-normal kernels shared by JAGS and R-native calls.
• adds native selected-normal routines for log likelihoods, normalizers, CDFs, moments, RNG, weighted summaries, funnel contours, regplot intervals, and zplot densities/threshold summaries.
• adds native GLMM marginal and cluster log-likelihood helpers for binomial and Poisson models.
• caches selected-normal normalizers and uses telescoping selection probabilities with log-space fallbacks for better numerical stability.
• relocates selected-normal C++ code to src/selnorm/ and updates Makevars*, native registration, cleanup rules, and JAGS distribution registration.
• removes unused native matrix/LAPACK helper sources and older source-level transformation helpers.

Documentation and tests

• reorganizes vignettes into numbered workflows covering introduction, prior distributions, baseline Bayesian meta-analysis, feature coverage, metafor parity, model averaging, RoBMA, multilevel models, medicine examples, and zplot diagnostics.
• regenerates roxygen documentation for the new constructors, priors, predictions, summaries, diagnostics, plots, model-comparison methods, and datasets.
• refreshes the README and pkgdown site for the 4.0.0 API.
• adds cached model fits under numbered vignette/model directories.
• refactors tests into ordered input, fitting, prediction, plotting, diagnostics, model-comparison, selected-normal kernel, and vignette-cache coverage.
• adds regression tests for selected-normal telescope probabilities, native/R fallback parity, posterior-row alignment, GLMM response conversion, LOO/WAIC targets, bridge sampling, and visual outputs.

RoBMA 3.6.1

Features

• Explanation vignette that helps navigate users through the vignettes
• two vignettes demonstrating robust Bayesian meta-analysis and meta-regressions
• summary() function now provides publication bias model type summary (type = "models") for models fitted using algorithm = "ss"
• improves control over plot.zcurve_RoBMA (i.e., specifying col, border, etc for the individual elements)

RoBMA 3.6.0

Features

• funnel() plot to visualize residuals vs the expected sampling distribution for RoBMA() and RoBMA.reg() models when using the algorithm = "ss"
• residuals() method for RoBMA() and RoBMA.reg() models when using the algorithm = "ss"
• as_zcurve() function to transform meta-analytic models into a z-curve style object, only available for RoBMA() and RoBMA.reg() fitted using the algorithm = "ss"
• plot(), summary(), and print() functions for the as_zcurve objects

RoBMA 3.5.1

Features

• summary() function now supports a standardized_coefficients argument to report either standardized (default) or raw meta-regression coefficients
• extract() function to extract the posterior samples of the model parameters
• true_effects() function to summarize the true effect size estimates of RoBMA() and RoBMA.reg() models when using the algorithm = "ss"
• predict() method for RoBMA() and RoBMA.reg() models when using the algorithm = "ss"

Fixes

• fitting a meta-regression using predictors with missing values result in a clear error message

Changes

• improving the speed of unit tests

RoBMA 3.5.0

version 3.5

Features

• approximate and computationally feasibly 3lvl selection models via the RoBMA() and RoBMA.reg() functions with the study_ids argument when using algorithm = "ss"
• 3lvl binomial-normal models for binary data via the BiBMA and BiBMA.reg functions with the study_ids argument when using algorithm = "ss"
• pooled_effect() function to compute the pooled effect size from the RoBMA.reg, NoBMA.reg, and BiBMA.reg models
• adjusted_effect() function to compute the adjusted effect size from the RoBMA.reg, NoBMA.reg, and BiBMA.reg models
• enables summary_heterogeneity() for BiBMA models

Fixes

• passing and checks of the study_ids and study_labels arguments
• PEESE prior distribution now scale as 1/scale instead of 1/scale^2 with the rescale_priors argument
• the conditional prediction interval based on summary_heterogeneity() is now conditional on the presence of the effect
• additional minor prior handling fixes (i.e., missing marginal estimates when only alternative prior distributions were specified etc)
• diagnostics with mixture baseline priors when using algorithm = "ss"
• summary_heterogeneity() with only a single study does not produce relative heterogeneity instead of crashing

RoBMA 3.4.0

Features

• adding binomial-normal meta-regression models for binary data via the BiBMA.reg function
• the spike and slab algorithm for faster model estimation via the algorithm = "ss" argument for BiBMA models
• default prior distributions for all parameters of BiBMA models are now set via the set_default_binomial_priors() function

RoBMA 3.3.0

Features

• the spike and slab algorithm for faster model estimation via the algorithm = "ss" argument (see a new vignette for more details)
• refactoring of the JAGS C++ code of weighted distributions and exporting of the lpdfs into JAGS (maintenance)
• weights_mix JAGS prior distribution to sample a mixture of weight functions directly

Fixes

• incorrectly omitting models with more than one predictor when computing conditional marginal summary

RoBMA 3.2.0

Features

• summary_heterogeneity() function to summarize the heterogeneity of the RoBMA models (prediction interval, tau, tau^2, I^2, and H^2)
• check_RoBMA_convergence() function to check the convergence of the RoBMA models
• adds informed prior distributions for binary and time-to-event outcomes via BayesTools 0.2.17

Fixes

• checking and fixing the number of available cores upon loading the package (hopefully fixes some parallelization issues)
• update() function re-evaluates convergence checks of individual models (#34)
• typos and minor issues in the vignettes

RoBMA 3.1.0

Features

• binomial-normal models for binary data via the BiBMA function
• NoBMA and NoBMA.reg() functions as wrappers around RoBMA RoBMA.reg() functions for simpler specification of publication bias unadjusted Bayesian model-averaged meta-analysis
• adding odds ratios output transformation`
• extending (instead of a complete refitting) of models via the update.RoBMA() function (only non-converged models by default or all by setting extend_all = TRUE)

Fixes

• handling of non-converged models

RoBMA 3.0.1

Fixes (thanks to Don & Rens)

• compilation issues with Clang (#28)
• lapack path specifications (#24)