- inability to fit a model without specifying a seed
- inability to produce individual model plots due to incompatibility with the newer versions of ggplot2

- parallel within and between model fitting using the parallel package with ‘parallel = TRUE’ argument

- 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

- inability to run models without the silent = TRUE control

- 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

- marginal likelihood calculation for models with spike prior distribution on mean parameter which location was not set to 0
- some additional error messages

- changing information messages from ‘cat’ to ‘message’ from plot related functions
- saving and returning the ‘par’ settings to the user defined one in the base plot functions

- the summary and plot function now shows quantile based confidence intervals for individual models instead of the HPD provided before (this affects only ‘summary’/‘plot’ with ‘type = “individual”’, all other confidence intervals were quantile based before)

- summary function returning median instead of mean

- 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

- analyzing OR
- distributions implemented using boost library (helps with convergence issues)
- ability to mute the non-suppressible “precision not achieved” warning messages by using “silent” = TRUE inside of the control argument
- vignettes

- the way how the seed is set before model fitting (the simulation study will not be reproducible with the new version of the package)