reReg

Regression models for recurrent event data

reReg implements a collection of regression models for recurrent event process and failure time. The package is still under active development.

Installation

You can install and load reReg from CRAN using

install.packages("reReg")
library(reReg)

You can install reReg from github with:

## install.packages("devtools")
devtools::install_github("stc04003/reReg", ref = "main")

Online documentation

Online document includes:

• Package vignette on Creating Recur objects.
• Package vignette on visualization of recurrent event data.
• Package vignette on simulating recurrent event data.
• Package vignette on regression analysis for recurrent event data.

References:

Lin, D., Wei, L., Yang, I. and Ying, Z. (2000). Semiparametric Regression for the Mean and Rate Functions of Recurrent Events. Journal of the Royal Statistical Society: Series B (Methodological), 62: 711-730.

Wang, M.-C., Qin, J., and Chiang, C.-T. (2001). Analyzing Recurrent Event Data with Informative Censoring. Journal of the American Statistical Association 96(455): 1057-1065.

Ghosh, D. and D.Y. Lin (2002). Marginal Regression Models for Recurrent and Terminal Events. Statistica Sinica, 663-688.

Ghosh, D. and D.Y. Lin (2003). Semiparametric Analysis of Recurrent Events Data in the Presence of Dependent Censoring. Biometrics, 59: 877-885.

Huang, C.-Y. and Wang, M.-C. (2004). Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data. Journal of the American Statistical Association 99(468), 1153-1165.

Xu, G., Chiou, S.H., Huang, C.-Y., Wang, M.-C. and Yan, J. (2017). Joint Scale-change Models for Recurrent Events and Failure Time. Journal of the American Statistical Association 112(518): 796-805.

Xu, G., Chiou, S.H., Yan, J., Marr, K., and Huang, C.-Y. (2020) Generalized Scale-Change Models for Recurrent Event Processes under Informative Censoring. Statistica Sinica 30, 1773–1795.