The goal of cqrIS is to fit a censored quantile regression model to ordinary survival data. It also provides tools to fit a censored quantile regression model to functional covariates and a recurrent event model to doubly-censored recurrent events. Improvements on interquantile smoothness are also available in this package.

You can install the released version of cqrIS from GitHub with:

devtools::install_github("ZexiCAI/cqrIS")
library(cqrIS)

This is a basic example which shows you how to fit a censored quantile regression model to ordinary survival data, and obtain its smoothed version:

library(cqrIS)
dat <- ordin.sam200.cen25.homo
res.PH <- estPH(Z=dat[,-c(1:2)], X=dat[,1], cen=dat[,2])
est.PH <- res.PH[[1]]

res.cqrIS <- cqrIS(Z=dat[,-c(1:2)], X=dat[,1], cen=dat[,2])
est.cqrIS <- res.cqrIS[[1]]

See the package vignette for more information and detailed instruction.

• Cai, Z. and Sit, T. (2020+), “Censored Quantile regression with Induced Smoothing,” Working Paper.

• Jiang, F., Cheng, Q., Yin, G. and Shen, H. (2020), “Generalizing Quantile Regression for Counting Processes with Applications to Recurrent Events,” Journal of the American Statistical Association, 115, 931-944.

• Peng, L. and Huang, Y. (2008), “Survival Analysis with Quantile Regression Models,” Journal of the American Statistical Association, 103, 637-649.

• Sun, X., Peng, L., Huang, Y. and Lai, H.J. (2016), “Generalizing Quantile Regression for Counting Processes with Applications to Recurrent Events,” Journal of the American Statistical Association, 111, 145-156.