# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "aiDIF" in publications use:' type: software license: GPL-3.0-or-later title: 'aiDIF: Differential Item Functioning for AI-Scored Assessments' version: 0.2.0 doi: 10.32614/CRAN.package.aiDIF identifiers: - type: doi value: 10.32614/CRAN.package.aiDIF abstract: Detects and quantifies differential item functioning (DIF) in AI-scored educational and psychological assessments. Provides a fully self-contained robust DIF engine (M-estimation via iteratively re-weighted least squares with the bi-square loss) alongside the Differential AI Scoring Bias (DASB) test, which detects item-level scoring shifts that differ across subgroups when comparing human and AI scoring conditions. Supports independent and paired scoring designs, robust linking of the cross-condition contrast, multiplicity control, conversion of fitted 'mirt' models to package inputs, simulation utilities, anchor weight diagnostics, and an AI-effect classification framework. Methods follow Halpin (2024) . authors: - family-names: Hait given-names: Subir email: haitsubi@msu.edu orcid: https://orcid.org/0009-0004-9871-9677 preferred-citation: type: manual title: 'aiDIF: Differential Item Functioning for AI-Scored Assessments' authors: - family-names: Hait given-names: Subir orcid: https://orcid.org/0009-0004-9871-9677 email: haitsubi@msu.edu year: '2026' notes: R package version 0.2.0 doi: 10.32614/CRAN.package.aiDIF url: https://CRAN.R-project.org/package=aiDIF repository: https://subirhait.r-universe.dev repository-code: https://github.com/causalfragility-lab/aiDIF commit: 6668f5f135463a547248481e859474bc83d20cfc url: https://github.com/causalfragility-lab/aiDIF date-released: '2026-09-04' contact: - family-names: Hait given-names: Subir email: haitsubi@msu.edu orcid: https://orcid.org/0009-0004-9871-9677