`NEWS.md`

- 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`

)

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

- 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

- 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.

- 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).

- 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"`

- 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)

- 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

Please notice that this is a major release that breaks backwards compatibility.

- 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"`

.

- the studies’s true effects are now marginalized out of the random effects models and are no longer estimated (see Appendix A of our prerint for more details). As a results, arguments referring to the true effects are now disabled.
- all models are now being estimated using the likelihood of effect sizes (instead of test-statistics as usually defined). We reproduced the simulation study that we used to evaluate the method performance and it achieved identical results (up to MCMC error, before marginalizing out the true effects). A big advantage of using the normal likelihood for effect sizes is a considerable speed up of the whole estimation process.
- as a results of these two changes, the results of the models will differ to those of pre 1.2.0 version

- models being fitted automatically until reaching R-hat lower than 1.05 without setting max_rhat and autofit control parameters
- bug preventing to draw a bivariate plot of mu and tau
- range for parameter estimates from individual models no containing 0 (or 1 in case of OR measured effect sizes)
- inability to fit a model with only null mu distributions if correlation or OR measured effect sizes were specified
- ordering of the estimated and observed effects when both of them are requested simultaneously
- formatting of this file (NEWS.md)

- priors plot: parameter specification, default plotting range, clearer x-axis labels in cases when the parameter is defined on transformed scale
- parameters plots: probability scale always ends at the same spot as is the last tick on the density scale
- adding warnings if any of the specified models has Rhat higher than 1.05 or the specified value
- grouping the same warnings messages together

- x-axis rescaling for the weight function plot (by setting ‘rescale_x = TRUE’ in the ‘plot.RoBMA’ function)
- setting expected direction of the effect in for RoBMA function

- incorrectly weighted theta estimates
- models with non-zero point prior distribution incorrectly plotted using when “models” option in case that the mu parameter was transformed