Bayesian Q Methodology: Exact Rank-Order Likelihood for Forced Q Sorts R package bayesqm version 0.2.0 A Bayesian analysis for Q methodology, alongside the classical one. Models the forced Q sort as an ordered partition of the statements through an exact rank-order likelihood (the design quotas fix the partition margins, so the likelihood of the observed sorting event is exact), fits it by a parameter-expanded Gibbs sampler in R with no compiled code and a convergence gate on rotation-invariant functionals, resolves rotational ambiguity via the MatchAlign post-processing of Poworoznek et al. (2025) <doi:10.1214/25-BA1544>, and returns the familiar Q tables as posterior summaries: credible intervals for bounded participant loadings, flag probabilities with an explicit unclassified state, quota-respecting factor arrays, distinguishing and consensus statements judged against a posterior critical difference and a grid-width equivalence region, one posterior false-discovery rule for all published claims, and a two-signal posterior-predictive workflow for the number of factors. Software Depends: R (>= 4.1.0) Imports: stats, utils, tools, clue, posterior (>= 1.5.0) Suggests: loo (>= 2.7.0), readxl (>= 1.4.0), jsonlite (>= 1.8.0), ggplot2 (>= 3.4.0), knitr (>= 1.40), rmarkdown (>= 2.20), testthat (>= 3.0.0) Raymond Dacosta Azadda <rdazadda@alaska.edu> Comprehensive R Archive Network (CRAN) Raymond Dacosta Azadda [aut, cre] (ORCID: <https://orcid.org/0009-0002-4384-4556>), Henry Ofoe Agbi-Kaiser [aut] (ORCID: <https://orcid.org/0009-0008-6127-9136>), Hannah D. Robinson [aut] (ORCID: <https://orcid.org/0009-0000-5159-1363>), AK-ACE Team [aut], Karsten Hueffer [aut], Taa'aii Peter [aut], Stacy Rasmus [aut] GPL (>= 3) 2026-08-20 application/tgz https://CRAN.R-project.org/package=bayesqm doi:10.32614/CRAN.package.bayesqm en-US