Provides a reproducible pipeline for deriving two clinically meaningful cutoffs 
    for disease biomarkers using a unified two-stage framework. The package integrates finite
    mixture modeling with risk prediction using biomarker plus clinical features, followed by decision 
    curve analysis to evaluate clinical utility. Outputs include biomarker density plots, 
    risk calibration curves, decision curves, and summary tables of diagnostic performance. 
    Designed for researchers in bio-statistics, neurology, and data science, this package
    emphasizes reproducibility, transparency, and clear clinical relevance.

Provides a calculator for the two-dimensional clinical Disease Activity
    index for Psoriatic Arthritis (TwoDcDAPSA), a principal component-derived measure
    that complements the conventional clinical DAPSA score. TwoDcDAPSA captures
    residual variation in patient-reported outcomes (pain and patient global assessment)
    and joint counts (swollen and tender) after adjusting for standardized cDAPSA using
    natural spline coefficients derived from published models. Residuals are
    standardized and combined with fixed principal component loadings to yield two
    continuous component scores: the PROs-Joint Contrast (PJC) and the
    Swollen–Tender joints Contrast (STC), along with quartile-based groupings
    (including optional combined quartile groupings). The package applies pre-specified
    coefficients, residual standardization, and loadings to new datasets but does not
    estimate spline models or principal components itself.

Provides tools for constructing conditional two-dimensional reference regions in continuous data, particularly suited for clinical, biological, or epidemiological studies requiring robust multivariate assessment. The implemented methodology combines directional quantiles with median‑based partial correlation models to produce reliable and interpretable reference regions even in the presence of outliers. Key features include robust conditional modeling for two responses conditioned on covariates, directional quantile regions, cross‑validation of coverage, visualization tools, and flexible formula‑based inputs.