Implements the S-type estimators, novel robust estimators for general linear regression models, addressing challenges such as outlier contamination and leverage points. This package introduces robust regression techniques to provide a robust alternative to classical methods and includes diagnostic tools for assessing model fit and performance. The methodology is based on the study, "Comparison of the Robust Methods in the General Linear Regression Model" by Sazak and Mutlu (2023). This package is designed for statisticians and applied researchers seeking advanced tools for robust regression analysis.

Implements S-type ridge regression, a robust and
 multicollinearity-aware linear regression estimator that combines S-type
 robust weighting (via the 'Stype.est' package) with ridge penalization;
 automatically selects the ridge parameter using the 'ridgregextra'
 approach targeting a close to 1 variance inflation factor (VIF), and returns
 comprehensive outputs (coefficients, fitted values, residuals, mean
 squared error (MSE), etc.) with an easy x/y interface and optional
 user-supplied weights. See Sazak and Mutlu (2021)
 <doi:10.1080/03610918.2021.1928196>, Karadag et al. (2023)
 <https://CRAN.R-project.org/package=ridgregextra> and Sazak et al. (2025)
 <https://CRAN.R-project.org/package=Stype.est>.

A test for the existence of a subgroup with enhanced treatment 
    effect. And, a sample size calculation procedure for the subgroup 
    detection test.

Produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.

Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.