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>.