Description: Extension of cmprsk to Stratified and Clustered data.
Goodness of fit test for Fine-Gray model.

Title: Bias-Corrected Variance for Competing Risks Regression with
Authors@R: c(person("Xinyuan", "Chen", role = c("aut","cre","cph"),
A user friendly function 'crrcbcv' to compute bias-corrected variances for competing risks regression models using proportional subdistribution hazards with small-sample clustered data. Four types of bias correction are included: the MD-type bias correction by Mancl and DeRouen (2001) <doi:10.1111/j.0006-341X.2001.00126.x>, the KC-type bias correction by Kauermann and Carroll (2001) <doi:10.1198/016214501753382309>, the FG-type bias correction by Fay and Graubard (2001) <doi:10.1111/j.0006-341X.2001.01198.x>, and the MBN-type bias correction by Morel, Bokossa, and Neerchal (2003) <doi:10.1002/bimj.200390021>.
Depends: R (>= 3.5.0), crrSC, abind, pracma, survival, stats

Title: Penalized Variable Selection in Competing Risks Regression
Description: 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.

Title: Stepwise covariate selection for the Fine & Gray competing risks
Description: 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

Description: 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.

Title: Stepwise Covariate Selection for the Fine & Gray Competing Risks

Author: Jeffrey S. Racine <racinej@mcmaster.ca> and Zhenghua Nie
Maintainer: Jeffrey S. Racine <racinej@mcmaster.ca>
Description: 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