Penalized Variable Selection in Competing Risks Regression
In competing risks regression, the proportional subdistribution hazards(PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for penalized variable selection for the PSH model. Penalties include LASSO, SCAD, MCP, and their group versions.

Stepwise covariate selection for the Fine & Gray competing risks
regression model
Performs forward and backwards stepwise regression for the
Proportional subdistribution hazards model in competing risks
(Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as
selection criteria. BICcr has a penalty of k = log(n*), where
n* is the number of primary events.

Performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events.

Stepwise Covariate Selection for the Fine & Gray Competing Risks
Regression Model

Jeffrey S. Racine <racinej@mcmaster.ca> and Zhenghua Nie
<niez@mcmaster.ca>
This package provides for estimating regression splines
that handle a mix of continuous and categorical (discrete) data
often encountered in applied settings. I would like to
gratefully acknowledge support from the Natural Sciences and
Engineering Research Council of Canada (NSERC:www.nserc.ca),
the Social Sciences and Humanities Research Council of Canada
(SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic
Research Computing Network (SHARCNET:www.sharcnet.ca).

Jeffrey S. Racine <racinej@mcmaster.ca> and Zhenghua Nie <niez@mcmaster.ca>
This package provides for estimating regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from  the Natural Sciences and Engineering Research Council of Canada (NSERC:www.nserc.ca), the Social Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca).