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 (2017) 
    <doi:10.3758/s13428-017-0967-6>. The hybrid method is a meta-analysis method 
    for combining an original study and replication and while taking into account 
    statistical significance of the  original study. 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).