Package: phacking
Title: Sensitivity Analysis for p-Hacking in Meta-Analyses
Version: 0.2.1
Authors@R: c(
  person("Peter", "Solymos", email = "peter@analythium.io", role = c("cre", "ctb"), comment = c(ORCID = "0000-0001-7337-1740")),
  person("Maya", "Mathur", email = "mmathur@stanford.edu", role = c("aut")),
  person("Mika", "Braginsky", email = "mika.br@gmail.com", role = c("aut"))
  )
Description: Fits right-truncated meta-analysis (RTMA), a bias
        correction for the joint effects of p-hacking (i.e.,
        manipulation of results within studies to obtain significant,
        positive estimates) and traditional publication bias (i.e., the
        selective publication of studies with significant, positive
        results) in meta-analyses [see Mathur MB (2022). "Sensitivity
        analysis for p-hacking in meta-analyses."
        <doi:10.31219/osf.io/ezjsx>.]. Unlike publication bias alone,
        p-hacking that favors significant, positive results (termed
        "affirmative") can distort the distribution of affirmative
        results. To bias-correct results from affirmative studies would
        require strong assumptions on the exact nature of p-hacking. In
        contrast, joint p-hacking and publication bias do not distort
        the distribution of published nonaffirmative results when there
        is stringent p-hacking (e.g., investigators who hack always
        eventually obtain an affirmative result) or when there is
        stringent publication bias (e.g., nonaffirmative results from
        hacked studies are never published). This means that any
        published nonaffirmative results are from unhacked studies.
        Under these assumptions, RTMA involves analyzing only the
        published nonaffirmative results to essentially impute the full
        underlying distribution of all results prior to selection due
        to p-hacking and/or publication bias. The package also provides
        diagnostic plots described in Mathur (2022).
License: MIT + file LICENSE
URL: https://github.com/mathurlabstanford/phacking,
        https://mathurlabstanford.github.io/phacking/
BugReports: https://github.com/mathurlabstanford/phacking/issues
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
RdMacros: Rdpack
Biarch: true
Depends: R (>= 4.1.0)
Imports: dplyr, ggplot2, metabias, metafor, methods, purrr, rlang,
        stats, stats4, truncnorm, Rcpp (>= 0.12.0), RcppParallel (>=
        5.0.1), Rdpack, rstan (>= 2.18.1), rstantools (>= 2.2.0)
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
        RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>=
        2.18.0)
SystemRequirements: GNU make
LazyData: true
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Config/pak/sysreqs: make
Repository: https://mathurlabstanford.r-universe.dev
Date/Publication: 2023-07-15 21:14:15 UTC
RemoteUrl: https://github.com/mathurlabstanford/phacking
RemoteRef: HEAD
RemoteSha: c5cc0ed2438806153e230662ee20c194fb48bec5
NeedsCompilation: yes
Packaged: 2026-06-24 09:27:56 UTC; root
Author: Peter Solymos [cre, ctb] (ORCID:
    <https://orcid.org/0000-0001-7337-1740>),
  Maya Mathur [aut],
  Mika Braginsky [aut]
Maintainer: Peter Solymos <peter@analythium.io>