Provides statistical auditing, risk documentation, and reporting
    tools to support AI governance workflows for employment and hiring decision
    systems. Implements the EEOC four-fifths adverse impact rule
    (Equal Employment Opportunity Commission, 1978,
    <https://www.ecfr.gov/current/title-29/subtitle-B/chapter-XIV/part-1607>),
    NYC Local Law 144 bias audit requirements (New York City, 2023,
    <https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page>),
    and the AI Risk Management Framework checklist items from the National
    Institute of Standards and Technology
    (2023, <doi:10.6028/NIST.AI.100-1>). Optionally supports EU AI Act
    high-risk classification (European Parliament and Council, 2024,
    <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689>).
    The package does not provide legal advice or certify legal compliance; it
    is a statistical and documentation support tool.

Provides tools for validating, generating, reviewing, reporting,
    and visualising assessment item generation workflows. The package supports
    tabular item-bank templates, item-bank validation, 'Python'-backed agentic
    generation workflows, multimodal diagram generation, quality summaries, and
    'HTML' reporting. External artificial intelligence services and related
    'API' calls require user-supplied credentials and are not called during
    package checks. The workflow is informed by automatic item generation
    methods described by Gierl and Haladyna (2013, ISBN:9780415897518) and
    evidence-centered assessment design described by Mislevy et al. (2003)
    <doi:10.1002/j.2333-8504.2003.tb01908.x>.

Provides statistical methods for analyzing experimental 
    evaluation of the causal impacts of algorithmic recommendations 
    on human decisions developed by Imai, Jiang, Greiner, Halen, and 
    Shin (2023) <doi:10.1093/jrsssa/qnad010> and Ben-Michael, Greiner,  
    Huang, Imai, Jiang, and Shin (2024) <doi:10.48550/arXiv.2403.12108>.
    The data used for this paper, and made available here, are interim, 
    based on only half of the observations in the study and (for those 
    observations) only half of the study follow-up period. We use them 
    only to illustrate methods, not to draw substantive conclusions.