Updated and improved documentation
Include a new implementation of the hybrid() function such that it also works for multiple conventional and preregistered studies.
The R package “metadat” is now included as suggested package, because the data from Lehmann et al. (2018) are now used as example in the documentation of the hybrid() function.
Fix a bug in hybrid() where the standard error was also transformed from Fisher’s z-value to Pearson correlation
Improve pdist_hy() such that logarithm of the probabilities are computed. The approximation of extreme tail probabilities is now no longer needed.
hybrid() now has a control argument that provide the user more control about the estimation. Note that this may cause minor differences in estimates compared to the previous version due to different bounds that are used for estimation.
The internal function bounds_hy() is no longer used for determining the bounds for estimation and is removed from the package
The default optimization procedure of puni_star() and method = “ML” is now to optimize both parameters at the same time. The previous version where the profile log-likelihood functions of both parameters were iteratively optimized is still available by setting the control argument proc.ml = “prof”.
Fix a bug in the computation of the profile likelihood confidence intervals of esest_nsig() and in the computation of the likelihood-ratio test in testeffect_nsig() and testhetero(). Note that the functions get_LR_est() and get_LR_tau() are not necessary anymore and are deleted from the package.
Updated and improved documentation
Modifying meta_plot() function to avoid a warning by the cumul() function of the metafor package
Updated and improved documentation
pub_bias argument in meta_plot() function is now more flexible
Publication bias test of puni_star() removed for now
Updated and improved documentation
Added extra information to the plot created with the meta_plot() function
Bug fix for conducting likelihood ratio tests with the puni_star() function
Updated and improved documentation
var_dif_fis(), var_boot_fis(), var_dif_rmd(), var_boot_rmd(), and var_pop() were added to estimate the variability of the outcomes’ effect size. These functions can be used in a meta-regression model to correct for outcome reporting bias with the CORB method.
Updated and improved documentation
Setting default parameters in puniform(), hybrid(), snapshot(), and puni_star() without losing backwards compatibility