Estimation of Bayes and local Bayes false discovery rates for
      replicability analysis (Heller & Yekutieli, 2014 <doi:10.1214/13-AOAS697> ;
      Heller at al., 2015 <doi: 10.1093/bioinformatics/btu434>).

Updates values within csv format data files using a custom, User-built
    csv format lookup file. Based on 'data.table' package.

Create presentations and display them inside the R 'REPL'
    (Read-Eval-Print loop), aka the R console. Presentations can be written in
    'RMarkdown' or any other text format. A set of convenient navigation options
    as well as code evaluation during a presentation is provided. It is great
    for tech talks with live coding examples and tutorials. While this is not a
    replacement for standard presentation formats, it's old-school looks might
    just be what sets it apart. This project has been inspired by the
    'REPLesent' project for presentations in the 'Scala' 'REPL'.

For a multisite replication project, computes the consistency metric P_orig, which is
    the probability that the original study would observe an estimated effect size as extreme or more extreme
    than it actually did, if in fact the original study were statistically consistent with the replications. Other
    recommended metrics are: (1) the probability of a true effect of scientifically meaningful size in the same direction as the estimate
    the original study; and (2) the probability of a true effect of meaningful size in the direction opposite
    the original study's estimate. These two can be computed using the package \code{MetaUtility::prop_stronger}.
    Additionally computes older metrics used in replication projects (namely expected agreement in "statistical significance" between an original study and replication studies as well
    as prediction intervals for the replication estimates). See Mathur and VanderWeele (under review; <https://osf.io/apnjk/>)
    for details.

Performs comparative bioavailability calculations for Average
    Bioequivalence with Expanding Limits (ABEL). Implemented are 'Method A' /
    'Method B' and the detection of outliers. If the design allows, assessment
    of the empiric Type I Error and iteratively adjusting alpha to control the
    consumer risk. Average Bioequivalence - optionally with a tighter (narrow
    therapeutic index drugs) or wider acceptance range (South Africa: Cmax) -
    is implemented as well.