Statistical and Psychometric Evaluation of AI Systems R package aiEvalR version 0.1.0 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, <ISBN:9780471188506>) and Brennan (2001) <doi:10.1007/978-1-4757-3456-0>. Software Depends: R (>= 4.1.0) Imports: stats, utils Suggests: testthat (>= 3.0.0), knitr, rmarkdown, covr, boot, lme4, dplyr, ggplot2, aiDIF, spelling Subir Hait <haitsubi@msu.edu> Comprehensive R Archive Network (CRAN) Subir Hait [aut, cre] (ORCID: <https://orcid.org/0009-0004-9871-9677>) MIT + file LICENSE (https://CRAN.R-project.org/package=aiEvalR/LICENSE) 2026-08-30 application/tgz https://CRAN.R-project.org/package=aiEvalR doi:10.32614/CRAN.package.aiEvalR en-US