aiEvalR
Statistical and Psychometric Evaluation of AI Systems
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | aiEvalR_0.1.0.tar.gz |
123.5 KiB |
0.1.0 |
rolling linux/noble R-4.5 | aiEvalR_0.1.0.tar.gz |
123.4 KiB |
0.1.0 |
rolling source/ R- | aiEvalR_0.1.0.tar.gz |
52.3 KiB |
0.1.0 |
latest linux/jammy R-4.5 | aiEvalR_0.1.0.tar.gz |
123.5 KiB |
0.1.0 |
latest linux/noble R-4.5 | aiEvalR_0.1.0.tar.gz |
123.4 KiB |
0.1.0 |
latest source/ R- | aiEvalR_0.1.0.tar.gz |
52.3 KiB |
0.1.0 |
2026-04-23 source/ R- | aiEvalR_0.1.0.tar.gz |
0 B |