fastcmprsk v1.1.1


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Fine-Gray Regression via Forward-Backward Scan

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 both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net.



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fastcmprsk is an R package for performing Fine-Gray regression via a forward-backward scan algorithm.

Official release is available on CRAN and the master branch on GitHub.


  • Scalable Fine-Gray estimation procedure for large-scale competing risks data.
  • Currently supports unpenalized and penalized (LASSO, ridge, SCAD, MCP, elastic-net) regression.
  • Can perform CIF estimation with interval/band estimation via bootstrap.

What’s New in Version 1.1.0?

  • Official version is loaded onto CRAN.


fastcmprsk in an R package with most functionality implemented in C. The package uses cyclic coordinate descent to optimize the likelihood function.


To install the latest development version, install from GitHub.


System Requirements

Requires R (version 3.5.0 or higher).

User Documentation


fastcmprsk is licensed under GPL-3.


fastcmprsk is being developed in R Studio.

Functions in fastcmprsk

Name Description
Crisk Create a Competing Risk Object
varianceControl Controls for Variance Calculation
coef.fcrrp Extract coefficients from an "fcrrp" object.
coef.fcrr Extract coefficients from an "fcrr" object.
summary.fcrr Summary method for fastCrr
plot.predict.fcrr Plots predicted cumulative incidence function
confint.fcrr Confidence Intervals for Model Parameters
plot.fcrrp Plots solution path for penalized methods
fastCrr Fast Fine-Gray Model Estimation
vcov.fcrr Extract variance-covariance matrix from an "fcrr" object.
fastCrrp Penalized Fine-Gray Model Estimation via two-way linear scan
simulateTwoCauseFineGrayModel Simulate data from the Fine-Gray Model
AIC.fcrr Akaike's An Information Criterion
predict.fcrr Cumulative Incidence Function Estimation
AIC.fcrrp Akaike's An Information Criterion
print.summary.fcrr Prints summary of a fcrr x
logLik.fcrr Extract log-pseudo likelihood from an "fcrr" object.
logLik.fcrrp Extract log-pseudo likelihood from an "fcrrp" object.
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Type Package
License GPL-3
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
NeedsCompilation yes
Packaged 2019-09-10 21:26:19 UTC; erickawaguchi
Repository CRAN
Date/Publication 2019-09-11 23:00:05 UTC

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