Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2022) <doi:10.32614/RJ-2022-043>, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.

Running Focused Identification of the Germplasm Strategy (FIGS) to make best subsets from Genebank Collection.

A general-purpose framework for Interpretable Contextual-Accountable
    and Responsible Machine Learning (ICARM) that works with any clean tabular
    data across any application domain including healthcare, finance, social
    science, business, and education. Automatically detects whether a prediction
    task is binary classification, multi-class classification, or regression
    from the target variable type. Provides a unified entry point icarm_fit()
    supporting both interpretable learners (Classification and Regression Trees
    (CART), logistic regression, linear regression, Generalized Additive Models
    (GAM)) and extended learners (random forest, 'XGBoost', Support Vector
    Machines (SVM)) with consistent interfaces for global and local model
    explanation including approximate SHapley Additive exPlanations (SHAP)
    values and Partial Dependence Profiles (PDPs), learning curve diagnostics,
    group-level fairness auditing across protected attributes, probability
    calibration, threshold analysis, multi-model comparison, reproducible
    JavaScript Object Notation (JSON) audit trails, and accountability
    scorecards. The contextual accountability framing emphasises that algorithmic
    fairness and interpretability requirements depend on the deployment domain
    and must be evaluated accordingly. Extends the 'civic.icarm' framework
    (Awe 2025) <https://cran.r-project.org/package=civic.icarm> to
    general-purpose applications beyond civic and political education.

Provides user-friendly tools for calibration in survey sampling.
    The package is production-oriented, and its interface is inspired by the famous
    popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to
    'icarus'. In addition to calibration (with linear, raking and logit methods),
    'icarus' features functions for calibration on tight bounds and penalized
    calibration.