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

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

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

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

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

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

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

Fixes (thanks to Don & Rens)

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

Features

• meta-regression with RoBMA.reg() function
• posterior marginal summary and plots for the RoBMA.reg models with summary_marginal() and plot_marginal() functions
• new vignette on hierarchical Bayesian model-averaged meta-analysis
• new vignette on robust Bayesian model-averaged meta-regression
• adding vignette from AMPPS tutorial
• faster implementation of JAGS multivariate normal distribution (based on the BUGS JAGS module)
• incorporating weight argument in the RoBMA and combine_data functions in order to pass custom likelihood weights
• ability to use inverse square weights in the weighted meta-analysis by setting a weighted_type = "inverse_sqrt" argument

Changes

• reworked interface for the hierarchical models. Prior distributions are now specified via the priors_hierarchical and priors_hierarchical_null arguments instead of priors_rho and priors_rho_null. The model summary now shows Hierarchical component summary.

Fixes

• suppressing start-up message
• cleaning up imports