Regression Families

Linear, logistic, survival, competing-risk, and Kaplan-Meier regression engines.

LinearRegression	LinearRegression implements Gaussian linear regression for associate$regression() . It supports covariate adjustment, split.by through the engine fit grid, and predictor-by-modifier interactions. Aliases: LinearRegression
LogisticRegression	LogisticRegression implements binomial generalized linear models for binary outcomes. It supports the same covariate, split.by , and interaction surface as LinearRegression, but reports estimates on the log-odds scale. Aliases: LogisticRegression
CoxRegression	CoxRegression implements right-censored Cox proportional-hazards models. It supports covariates, split.by through the engine fit grid, and predictor-by-modifier interactions in the Cox formula. Aliases: CoxRegression
CompetingRiskRegression	CompetingRiskRegression implements Fine-Gray subdistribution-hazards models through the suggested {cmprsk} package. Aliases: CompetingRiskRegression
KaplanMeierRegression	KaplanMeierRegression fits grouped Kaplan-Meier curves and reports omnibus log-rank statistics in the summary table. Group-specific curve steps, risk tables, and group summaries are exposed through artifacts. Aliases: KaplanMeierRegression