Functions to perform stepwise split regularized regression. The approach first 
             uses a stepwise algorithm to split the variables into the models with a goodness
             of fit criterion, and then regularization is applied to each model. The weights 
             of the models in the ensemble are determined based on a criterion selected by 
             the user.

Provides a complete analysis pipeline for the WHO STEPwise
    Approach to NCD Risk Factor Surveillance (STEPS) as described in
    Riley et al. (2016) <doi:10.2105/AJPH.2015.302962>. Imports raw
    survey data ('CSV', 'Excel', 'Stata', 'SPSS'), applies WHO-standard
    cleaning and recoding, sets up complex survey designs, computes all
    standard NCD indicators (tobacco, alcohol, diet, physical activity,
    anthropometry, blood pressure, biochemical), and generates
    publication-ready tables, visualisations, and 'Word'/'HTML' reports
    (fact sheet, data book, country report).

Provides reusable functions for aggregated cluster-period data,
    mixed-effects analysis, and simulation-based power and type I error
    evaluation in stepped-wedge cluster randomized trials. The design and
    mixed-effects analysis follow Hussey and Hughes (2007)
    <doi:10.1016/j.cct.2006.05.007>. Intraclass correlations for binary
    outcomes are converted to logistic-normal random-intercept standard
    deviations following Eldridge, Ukoumunne and Carlin (2009)
    <doi:10.1111/j.1751-5823.2009.00092.x>. Monte Carlo uncertainty in
    estimated power is summarized using the exact binomial interval of
    Clopper and Pearson (1934) <doi:10.1093/biomet/26.4.404>. The simulation
    engine supports sequence-specific baseline risks, cluster random effects,
    direct intraclass-correlation specification, Monte Carlo uncertainty
    intervals, and model-fitting diagnostics. Applied physician and specialty
    helpers are retained for backward compatibility and for an example
    health-services workflow.

Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.