Produces formal landscape PDF certificates with configurable
    color themes, optional logos, decorative borders, laurels, and corner
    ornaments. Built on 'grid' with no external 'LaTeX' or 'Quarto'
    dependency. Suitable for academic awards, professional recognition,
    and similar uses.

Provides tools to easily access and analyze Canadian Election Study data.
    The package simplifies the process of downloading, cleaning, and using 'CES' datasets
    for political science research and analysis. The Canadian Election Study ('CES')
    has been conducted during federal elections since 1965, surveying Canadians
    on their political preferences, engagement, and demographics. Data is accessed
    from multiple sources including the 'Borealis' Data repository 
    <https://borealisdata.ca/> and the official 'Canadian Election Study' website 
    <https://ces-eec.arts.ubc.ca/>. This package is not officially affiliated 
    with the Canadian Election Study, 'Borealis' Data, or the University of British 
    Columbia, and users should cite the original data sources in their work.

Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.

Collection of perceptually uniform colour maps made by Peter Kovesi
    (2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700> 
    at the Centre for Exploration Targeting (CET).

Ceteris Paribus Profiles (What-If Plots) are designed to present model 
    responses around selected points in a feature space. 
    For example around a single prediction for an interesting observation. 
    Plots are designed to work in a model-agnostic fashion, they are working 
    for any predictive Machine Learning model and allow for model comparisons.
    Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package.

Scalable inference for Varying Coefficient Mixed-Effects
    Models (VCMMs) with large, correlated random effects. Implements
    sufficient-statistics, one-step communication-efficient surrogate
    likelihood, and SVD-stabilized (Singular Value Decomposition)
    estimators with Kronecker and separable covariance structures.
    Implements the methodology of Jalili and Lin (2025)
    <doi:10.48550/arXiv.2511.12732>.