Package: speedyBBT
Title: Efficient Bayesian Inference for the Bradley--Terry Model
Version: 1.0.0.9000
Authors@R: 
    c(person(given = "Rowland",
           family = "Seymour",
           role = c("aut", "cre", "cph"),
           email = "r.g.seymour@bham.ac.uk",
           comment = c(ORCID = "0000-0002-8739-3921")),
      person(given = "Christine",
            family = "Stawitz",
            role = "aut",
            email = "c.c.stawitz@bham.ac.uk",
            comment = c(ORCID = "0000-0003-3122-4501")),
      person(given = "Heather", 
             family = "Turner", 
             role = "aut",
             email = "",
             comment = c(ORCID = "0000-0002-1256-3375")))
Description: A suite of functions that allow a full, fast, and
        efficient Bayesian treatment of the Bradley--Terry model. Prior
        assumptions about the model parameters can be encoded through a
        multivariate normal prior distribution. Inference is performed
        using a latent variable representation of the model.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
Imports: base, BayesLogit, coda, Matrix, stats, utils
Suggests: bayesplot, expm, ggplot2, knitr, rmarkdown, testthat (>=
        3.0.0), vdiffr
Depends: R (>= 2.10)
Config/testthat/edition: 3
URL: https://www.rowlandseymour.com/speedyBBT,
        https://github.com/rowlandseymour/speedyBBT
BugReports: https://github.com/rowlandseymour/speedyBBT/issues
Config/roxygen2/version: 8.0.0
VignetteBuilder: knitr
Repository: https://rowlandseymour.r-universe.dev
Date/Publication: 2026-09-23 15:44:35 UTC
RemoteUrl: https://github.com/rowlandseymour/speedybbt
RemoteRef: HEAD
RemoteSha: 004b99de238d9f66a34a2110c55f798f0084be83
NeedsCompilation: no
Packaged: 2026-09-23 17:34:13 UTC; root
Author: Rowland Seymour [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0002-8739-3921>),
  Christine Stawitz [aut] (ORCID:
    <https://orcid.org/0000-0003-3122-4501>),
  Heather Turner [aut] (ORCID: <https://orcid.org/0000-0002-1256-3375>)
Maintainer: Rowland Seymour <r.g.seymour@bham.ac.uk>