metaBMA 0.6.6
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* Upgrade to new rstantools folder structure
* New default prior for effect size: Cauchy distribution with scale=0.707 instead of normal distribution with scale=0.3
* New defaults in meta_default() wrapper function
* Faster CRAN checks and tests
metaBMA 0.6.4
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* bugfix: argument "ci" for credibility intervals not working
* reference added: Gronau et al. (2020)
metaBMA 0.6.3
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* increased stability and precision of model-averaged posterior distribution and estimates (based on density approximations)
metaBMA 0.6.2
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* Moderator analysis: rename slope parameters "alpha" to "beta"
* Bugfix for meta_bma(): Only use H0 models for averaging of "d" parameter
* New tests: Scheibehenne (2017)
metaBMA 0.6.0
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* new function meta_ordered() for order-constrained study-effects in random-effects meta-analysis
* table with estimates shows convergence statistics (Rhat, n_eff)
* meta_default(): new labels for effect = "d", "r", "z", "logOR"
* minor bugfixes and improvements
metaBMA 0.5.0
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* Major refactoring (breaks compatibiltiy with previous versions)
* Possible to provide data frame via argument 'data'
* Removed arguments "d.par" and "tau.par": priors are now defined via d=prior(...), tau=prior(...)
* Possibility to fit random and fixed effects meta-analysis with moderators in stan (with JZS priors)
* Computation of log marginal likelihood with Stan samples and bridge sampling (via logml="stan")
* Improved numerical integration via integrate() [posterior distribution shifted to zero]
metaBMA 0.3.9
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* Updated citation for CRAN
* Added examples for meta_bma() and meta_random()
* Minor bug fixes
metaBMA 0.3.8
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* Data sets 'power_pose' and 'power_pose_unfamiliar' added
* Data set 'facial_feedback' added
* More informative description file
* Requirements for CRAN
metaBMA 0.3.0
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* First stable version
* High-level functions meta_bma() and meta_default() perform model averaging for standard models (fixed, random + H0, H1)
* Plotting functions for averaged/random-effects/fixed-effects meta-analysis via plot_forest() and plot_posterior()
* Meta-analysis models are fitted by meta_fixed() and meta_random()
* Effect estimates of fitted meta-analysis models can be averaged by bma()
* Inclusion Bayes factor are computed by inclusion()
* User-specified and default prior functions are specified via prior() [can be plottet via plot(prior)]