# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "aiEvalR" in publications use:' type: software license: MIT title: 'aiEvalR: Statistical and Psychometric Evaluation of AI Systems' version: 0.1.0 doi: 10.32614/CRAN.package.aiEvalR abstract: Evaluates artificial intelligence (AI) systems as measurement instruments using psychometric methods. Provides multi-facet generalizability theory (G-study and D-study) via 'lme4', reliability via the intraclass correlation coefficient (ICC), calibration via the expected calibration error (ECE) and Brier score, robustness stress testing, and group disparity diagnostics. Item-level differential item functioning (DIF) based on item response theory (IRT) is delegated to the 'aiDIF' package. Methods follow Cronbach, Gleser, Nanda and Rajaratnam (1972, ) and Brennan (2001) . authors: - family-names: Hait given-names: Subir email: haitsubi@msu.edu orcid: https://orcid.org/0009-0004-9871-9677 repository: https://subirhait.r-universe.dev repository-code: https://github.com/causalfragility-lab/aiEvalR commit: 79c93e72ed8db61a7a8a91ce4d2cb6e19bfb9b52 url: https://github.com/causalfragility-lab/aiEvalR date-released: '2026-08-06' contact: - family-names: Hait given-names: Subir email: haitsubi@msu.edu orcid: https://orcid.org/0009-0004-9871-9677