{ "_id": "6a33b6d33efcd9bda43aeafa", "Package": "bayesqm", "Type": "Package", "Title": "Bayesian Q Methodology: Probabilistic Factor Analysis", "Version": "0.1.0", "Date": "2026-05-18", "Authors@R": "c(\nperson(given = \"Raymond Dacosta\", family = \"Azadda\",\nemail = \"rdazadda@alaska.edu\",\nrole = c(\"aut\", \"cre\")),\nperson(family = \"AK-ACE Team\", role = \"aut\"),\nperson(\"Karsten\", \"Hueffer\", role = \"aut\"),\nperson(\"Taa'aii\", \"Peter\", role = \"aut\"),\nperson(\"Stacy\", \"Rasmus\", role = \"aut\"))", "Description": "A Bayesian factor-analytic framework for Q methodology.\nFits a low-rank factor model to Q-sort data with a Student-t\nlikelihood and a hierarchical normal prior on loadings, samples\nthe posterior with Stan, resolves rotational ambiguity via the\nMatchAlign post-processing of Poworoznek et al. (2025)\n, and returns posterior summaries\nincluding credible intervals for loadings and factor scores,\nprobabilistic dominant-factor membership, distinguishing and\nconsensus statements, and PSIS-LOO-based factor enumeration\nfollowing Vehtari et al. (2017) \nwith the Sivula et al. 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