Package: BFF
Title: Bayes Factor Functions
Version: 4.4.1
Authors@R: 
    c(person(given = "Rachael",
           family = "Shudde",
           role = c("aut", "cre"),
           email = "rachael.shudde@gmail.com"),
           person(given = "František",
           family = "Bartoš",
           role = c("aut"),
           email = "f.bartos96@gmail.com"),
           person(given = "Saptati",
           family = "Datta",
           role = c("aut"),
           email = "saptati@tamu.edu"),
           person(given = "Riana",
           family = "Guha",
           role = c("aut"),
           email = "riana.guha@tamu.edu"),
           person(given = "Sandy", family = "Pramanik",
           role = c("aut"),
           email = "sandy.pramanik@gmail.com"),
           person(given = "Valen",
           family = "Johnson",
           role = c("aut"),
           email = "vejohnson@exchange.tamu.edu"))
Author: Rachael Shudde [aut, cre], František Bartoš [aut], Saptati
        Datta [aut], Riana Guha [aut], Sandy Pramanik [aut], Valen
        Johnson [aut]
Maintainer: Rachael Shudde <rachael.shudde@gmail.com>
Description: Bayes factors represent the ratio of probabilities
        assigned to data by competing scientific hypotheses. However,
        one drawback of Bayes factors is their dependence on prior
        specifications that define null and alternative hypotheses.
        Additionally, there are challenges in their computation. To
        address these issues, we define Bayes factor functions (BFFs)
        directly from common test statistics. BFFs express Bayes
        factors as a function of the prior densities used to define the
        alternative hypotheses. These prior densities are centered on
        standardized effects, which serve as indices for the BFF.
        Therefore, BFFs offer a summary of evidence in favor of
        alternative hypotheses that correspond to a range of
        scientifically interesting effect sizes. Such summaries remove
        the need for arbitrary thresholds to determine "statistical
        significance." BFFs are available in closed form and can be
        easily computed from z, t, chi-squared, and F statistics. They
        depend on hyperparameters "r" and "tau^2", which determine the
        shape and scale of the prior distributions defining the
        alternative hypotheses. Plots of BFFs versus effect size
        provide informative summaries of hypothesis tests that can be
        easily aggregated across studies.
License: GPL (>= 2)
URL: https://github.com/rshudde/BFF
BugReports: https://github.com/rshudde/BFF/issues
Encoding: UTF-8
Imports: grDevices, ggplot2, Matrix, gsl, stats, rlang
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BSDA, vdiffr
RoxygenNote: 7.3.1
VignetteBuilder: knitr
Depends: R (>= 2.10)
Config/testthat/edition: 3
Config/pak/sysreqs: libgsl0-dev
Repository: https://rshudde.r-universe.dev
Date/Publication: 2025-05-20 01:27:11 UTC
RemoteUrl: https://github.com/rshudde/bff
RemoteRef: HEAD
RemoteSha: aa9fead2c42871b800cd3d7a28ccaf3ad1f78710
NeedsCompilation: no
Packaged: 2026-10-13 08:37:57 UTC; root