RoBMA - 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

- R
Published by FBartos 7 months ago

RoBMA - 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

- R
Published by FBartos 9 months ago

RoBMA - 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

- R
Published by FBartos about 1 year ago

RoBMA - 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

- R
Published by FBartos about 1 year ago

RoBMA - 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 (https://github.com/FBartos/RoBMA/issues/34)
• typos and minor issues in the vignettes

- R
Published by FBartos about 1 year ago

RoBMA - 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

- R
Published by FBartos over 2 years ago

RoBMA - RoBMA 3.0.1

Fixes (thanks to Don & Rens)

• compilation issues with Clang (https://github.com/FBartos/RoBMA/issues/28)
• lapack path specifications (https://github.com/FBartos/RoBMA/issues/24)

- R
Published by FBartos over 2 years ago

RoBMA - RoBMA 3.0

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.

- R
Published by FBartos over 2 years ago

RoBMA - RoBMA 2.3.2

Fixes

• suppressing start-up message
• cleaning up imports

- R
Published by FBartos almost 3 years ago

RoBMA - RoBMA 2.3.1

Fixes

• fixing weighted meta-analysis parameterization

- R
Published by FBartos over 3 years ago

RoBMA - RoBMA 2.3

version 2.3

Features

• weighted meta-analysis by specifying study_ids argument in RoBMA and setting weighted = TRUE. The likelihood contribution of estimates from each study is down-weighted proportionally to the number of estimates in that study. Note that this experimental feature is supposed to provide a conservative alternative for estimating RoBMA in cases with multiple estimates from a study where the multivariate option is not computationally feasible.

- R
Published by FBartos over 3 years ago

RoBMA - RoBMA 2.0.0 - 2.2.2

Features

• three-level meta-analysis by specifying study_ids argument in RoBMA. However, note that this is (1) an experimental feature and (2) the computational expense of fitting selection models with clustering is extreme. As of now, it is almost impossible to have more than 2-3 estimates clustered within a single study).

Changes

• message about the effect size scale of parameter estimates is always shown
• compatibility with BayesTools 0.2.0+

Fixes

• updating the C++ to compile on M1 Mac

- R
Published by FBartos almost 4 years ago

RoBMA - RoBMA 2.1.2

Fixes

• adding Windows ucrt patch (thanks to Tomas Kalibera)

Updates

• adding BayesTools version check

- R
Published by FBartos about 4 years ago

RoBMA - RoBMA 2.1.1

Fixes

• incorrectly formatted citations in vignettes and capitalization

Features

• adding informed_prior() function (from the BayesTools package) that allows specification of various informed prior distributions from the field of medicine and psychology
• adding a vignette reproducing the example of dentine sensitivity with the informed Bayesian model-averaged meta-analysis from Bartoš et al., 2021 (open-access),
• further reductions of fitted object size when setting save = "min"

- R
Published by FBartos over 4 years ago

RoBMA - RoBMA 2.1

Fixes

• more informative error message when the JAGS module fails to load
• correcting wrong PEESE transformation for the individual models summaries (issue #12)
• fixing error message for missing conditional PET-PEESE
• fixing incorrect lower bound check for log(OR)

Features

• adding interpret() function (issue #11)
• adding effect size transformation via output_scale argument to plot() and plot_models() functions
• better handling of effect size transformations and scaling - BayesTools style back-end functions with Jacobian transformations

- R
Published by FBartos over 4 years ago

RoBMA - RoBMA 2.0

Changes - naming of the arguments specifying prior distributions for the different parameters/components of the models changed (priors_mu -> priors_effect, priors_tau -> priors_heterogeneity, and priors_omega -> priors_bias), - prior distributions for specifying weight functions now use a dedicated function (prior(distribution = "two.sided", parameters = ...) -> prior_weightfunction(distribution = "two.sided", parameters = ...)), - new dedicated function for specifying no publication bias adjustment component / no heterogeneity component (prior_none()), - new dedicated functions for specifying models with the PET and PEESE publication bias adjustments (prior_PET(distribution = "Cauchy", parameters = ...) and prior_PEESE(distribution = "Cauchy", parameters = ...)), - new default prior distribution specification for the publication bias adjustment part of the models (corresponding to the RoBMA-PSMA model from Bartoš et al., 2021 preprint), - new model_type argument allowing to specify different "pre-canned" models ("PSMA" = RoBMA-PSMA, "PP" = RoBMA-PP, "2w" = corresponding to Maier et al., in press , manuscript), - combine_data function allows combination of different effect sizes / variability measures into a common effect size measure (also used from within the RoBMA function), - better and improved automatic fitting procedure now enabled by default (can be turned of with autofit = FALSE) - prior distributions can be specified on the different scale than the supplied effect sizes (the package fits the model on Fisher's z scale and back transforms the results back to the scale that was used for prior distributions specification, Cohen's d by default, but both of them can be overwritten with the prior_scale and transformation arguments), - new prior distributions, e.g., beta or fixed weight functions, - estimates from individual models are now plotted with the plot_models() function and the forest plot can be obtained with the forest() function, - the posterior distribution plots for the individual weights are no able supported, however, the weightfunction and the PET-PEESE publication bias adjustments can be visualized with the plot.RoBMA() function and parameter = "weightfunction" and parameter = "PET-PEESE".

- R
Published by FBartos over 4 years ago