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RoBMA 4.0.0

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@FBartos FBartos released this 07 May 13:32
v4.0.0
09d07b9
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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.
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