Longitudinal Bias Auditing for Sequential Decision Systems R package AIBias version 0.1.0 Provides tools for detecting, quantifying, and visualizing algorithmic bias as a longitudinal process in repeated decision systems. Existing fairness metrics treat bias as a single-period snapshot; this package operationalizes the view that bias in sequential systems must be measured over time. Implements group-specific decision-rate trajectories, standardized disparity measures analogous to the standardized mean difference (Cohen, 1988, ISBN:0-8058-0283-5), cumulative bias burden, Markov-based transition disparity (recovery and retention gaps), and a dynamic amplification index that quantifies whether prior decisions compound current group inequality. The amplification framework extends longitudinal causal inference ideas from Robins (1986) <doi:10.1016/0270-0255(86)90088-6> and the sequential decision-process perspective in the fairness literature (see <https://fairmlbook.org>) to the audit setting. Covariate-adjusted trajectories are estimated via logistic regression, generalized additive models (Wood, 2017, <doi:10.1201/9781315370279>), or generalized linear mixed models (Bates, 2015, <doi:10.18637/jss.v067.i01>). Uncertainty quantification uses the cluster bootstrap (Cameron, 2008, <doi:10.1162/rest.90.3.414>). Software Depends: R (>= 4.1.0) Imports: dplyr (>= 1.1.0), tidyr (>= 1.3.0), ggplot2 (>= 3.4.0), rlang (>= 1.1.0), cli (>= 3.6.0), purrr (>= 1.0.0), tibble (>= 3.2.0) Suggests: mgcv, lme4, boot, knitr, rmarkdown, testthat (>= 3.0.0) 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=AIBias/LICENSE) 2026-04-04 application/tgz https://CRAN.R-project.org/package=AIBias doi:10.32614/CRAN.package.AIBias en-US