Computes a confidence interval for a specified linear combination of 
    the regression parameters in a linear regression model with iid normal 
    errors with unknown variance when there is uncertain prior information 
    that a distinct specified linear combination of the regression 
    parameters takes a specified number. This confidence interval, found by 
    numerical nonlinear constrained optimization, has the required minimum coverage 
    and utilizes this uncertain prior information through desirable 
    expected length properties. This confidence interval is proposed by 
    Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.

Implementation of the categorical instrumental variable (CIV) 
    estimator proposed by Wiemann (2023) <arXiv:2311.17021>. CIV allows for optimal instrumental 
    variable estimation in settings with relatively few observations per 
    category. To obtain valid inference in these challenging settings, CIV
    leverages a regularization assumption that implies existence of a latent 
    categorical variable with fixed finite support achieving the same first 
    stage fit as the observed instrument.

A general-purpose framework for Interpretable Civic-Accountable
    and Responsible Machine Learning (ICARM). Works with any clean tabular
    data and automatically detects whether a task is binary classification,
    multi-class classification, or regression from the target variable type.
    Provides a single unified entry point civic_fit() alongside tidy interfaces
    for global and local model explanations, group-level fairness auditing,
    probability calibration, multi-model comparison, threshold analysis, and
    reproducible audit trails. Designed to support the DataCitizen-Pro research
    agenda at Ludwigsburg University of Education: developing data literacy,
    statistical reasoning, and democratic judgment formation in civic and
    political teacher education.
    References: Biecek (2018) <doi:10.18637/jss.v085.i04>,
    Kuhn (2008) <doi:10.18637/jss.v028.i05>,
    Awe (2025) <https://github.com/Olawaleawe/civic.icarm>.