Nothing
pcdreg: Semiparametric Regression for Panel Count Data
Fits semiparametric regression models to panel count data, that is, to recurrent event processes observed only at intermittent examination times, allowing covariates to be time-varying. The proportional rate model is fitted by nonparametric maximum likelihood through an expectation-maximization algorithm under a working Poisson assumption, while inference does not require that assumption to hold; two computationally inexpensive covariance estimators are provided, a robust sandwich estimator that remains consistent when the Poisson assumption fails and an efficient information estimator that is a fast substitute for profile likelihood when it holds, with the profile likelihood estimator available for comparison. The proportional means model is fitted by the estimating equation of Hu, Sun and Wei (2003) <doi:10.1111/1467-9469.00316>. Methods for the rate model are described in Sun, Guo, Li, Tu and Sun (2024) <doi:10.3150/23-BEJ1713>.
Package details |
|
|---|---|
| Author | Dayu Sun [aut, cre] (ORCID: <https://orcid.org/0000-0002-7149-3303>), Yuanyuan Guo [ctb] (co-author of the method paper), Yang Li [ctb] (co-author of the method paper), Wanzhu Tu [ctb] (co-author of the method paper), Jianguo Sun [ctb] (co-author of the method paper) |
| Maintainer | Dayu Sun <dayusun@iu.edu> |
| License | GPL (>= 2) |
| Version | 0.1.0 |
| URL | https://github.com/dayusun/pcdreg https://www.sundayu.me/pcdreg/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the pcdreg package in your browser
Any scripts or data that you put into this service are public.
