Package: babebi
Type: Package
Title: Bayesian Estimation and Validation for Small-N Designs with
        Rater Bias
Version: 0.1.0
Authors@R: c(
    person(
      given = "Irene",
      family = "Gianeselli",
      email = "irene.gianeselli@unibz.it",
      role = c("aut", "cre"),
      comment = c(
        ORCID = "0000-0002-8689-3035",
        affiliation = "Free University of Bozen-Bolzano"
      )
    ),
    person(
      given = "Andrea",
      family = "Bosco",
      role = "aut",
      comment = c(
        ORCID = "0000-0002-9458-3993",
        affiliation = "University of Bari Aldo Moro"
      )
    ),
    person(
      given = "Demis",
      family = "Basso",
      role = "aut",
      comment = c(
        ORCID = "0000-0002-4595-3513",
        affiliation = "Free University of Bozen-Bolzano"
      )
    )
  )
Description: Approximate Bayesian inference and Monte Carlo validation for
    small-N repeated-measures designs with two time points and two raters.
    The package is intended for applications in which sample size is limited
    and the observed outcome may be affected by rater-specific bias.
    User-supplied data are standardised into a common long-format structure.
    Pre-post effects are analysed using difference scores in a linear model
    with a rater indicator as covariate. Posterior summaries for the
    regression coefficients are obtained from a large-sample normal
    approximation centred at the least-squares estimate with plug-in
    covariance under a flat improper prior. Evidence for a non-zero
    pre-post effect, adjusted for rater differences, is summarised using a
    BIC-based approximation to the Bayes factor for comparison between
    models with and without the pre-post effect. Monte Carlo validation uses
    design quantities estimated from the observed data, including sample
    size, mean pre-post change, and second-rater additive discrepancy, and
    summarises inferential performance in terms of bias, root mean squared
    error, credible interval coverage, posterior tail probabilities, and
    mean Bayes factor values. For background on the BIC approximation and
    Bayes factors, see Schwarz (1978)
    <doi:10.1214/aos/1176344136> and Kass and Raftery (1995)
    <doi:10.1080/01621459.1995.10476572>.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (>= 4.1.0)
Imports: stats, graphics
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-04-22 12:33:34 UTC; irene
Author: Irene Gianeselli [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-8689-3035>, affiliation: Free
    University of Bozen-Bolzano),
  Andrea Bosco [aut] (ORCID: <https://orcid.org/0000-0002-9458-3993>,
    affiliation: University of Bari Aldo Moro),
  Demis Basso [aut] (ORCID: <https://orcid.org/0000-0002-4595-3513>,
    affiliation: Free University of Bozen-Bolzano)
Maintainer: Irene Gianeselli <irene.gianeselli@unibz.it>
Repository: CRAN
Date/Publication: 2026-04-23 20:10:09 UTC
