Provides meta-analysis methods that correct for
    publication bias and outcome reporting bias. Four methods and a visual tool 
    are currently included in the package. The p-uniform method as described in 
    van Assen, van Aert, and Wicherts (2015) <doi:10.1037/met0000025> 
    can be used for estimating the average effect size, testing the null hypothesis 
    of no effect, and testing for publication bias using only the statistically 
    significant effect sizes of primary studies. The second method in the package 
    is the p-uniform* method as described in van Aert and van Assen (2023) 
    <doi:10.31222/osf.io/zqjr9>. This method is an extension of the p-uniform 
    method that allows for estimation of the average effect size and the 
    between-study variance in a meta-analysis, and uses both the statistically 
    significant and nonsignificant effect sizes. The third method in the package 
    is the hybrid method as described in van Aert and van Assen (2018) 
    <doi:10.3758/s13428-017-0967-6>. The hybrid method is a meta-analysis method 
    for combining a conventional study and replication/preregistered study while 
    taking into account statistical significance of the conventional study. This
    method was extended in van Aert (2025) <doi:10.1037/met0000719> 
    such that it allows for the inclusion of multiple conventional and 
    replication/preregistered studies. The p-uniform and hybrid method are based 
    on the statistical theory that the distribution of p-values is uniform 
    conditional on the population effect size. The fourth method in the package 
    is the Snapshot Bayesian Hybrid Meta-Analysis Method as described in van Aert 
    and van Assen (2018) <doi:10.1371/journal.pone.0175302>. This method computes 
    posterior probabilities for four true effect sizes (no, small, medium, and 
    large) based on an original study and replication while taking into account 
    publication bias in the original study. The method can also be used for 
    computing the required sample size of the replication akin to power analysis 
    in null-hypothesis significance testing. The meta-plot is a visual tool for 
    meta-analysis that provides information on the primary studies in the 
    meta-analysis, the results of the meta-analysis, and characteristics of the 
    research on the effect under study (van Assen et al., 2023). Helper functions 
    to apply the Correcting for Outcome Reporting Bias (CORB) method to correct 
    for outcome reporting bias in a meta-analysis (van Aert & Wicherts, 2023).