Longitudinal Bias Auditing for Sequential Decision Systems // 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).