cold-package: Count Longitudinal Data

In cold: Count Longitudinal Data

Description

Performs Poisson regression analysis for longitudinal count data, allowing for serial dependence among observations from a given individual and two random effects. Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed.

Details

This package contains functions to perform the fit of parametric models via likelihood method for count longitudinal data using "S4" classes and methods as implemented in the methods package.

Author(s)

M. Helena Gonçalves and M. Salomé Cabral

References

Azzalini, A. (1994). Logistic regression and other discrete data models for serially correlated observations. J. Ital. Stat. Society, 3 (2), 169-179. doi: 10.1007/bf02589225.

Gonçalves, M. Helena (2002). Likelihood methods for discrete longitudinal data. PhD thesis, Faculty of Sciences, University of Lisbon.

Gonçalves, M. Helena, Cabral, M. Salomé, Ruiz de Villa, M. Carme, Escrich, Eduardo and Solanas, Montse. (2007). Likelihood approach for count data in longitudinal experiments. Computational Statistics and Data Analysis, 51, 12, 6511-6520. doi: 10.1016/j.csda.2007.03.002.

Gonçalves, M. Helena and Cabral, M. Salomé. (2021). cold: An R Package for the Analysis of Count Longitudinal Data. Journal of Statistical Software, 99, 3, 1–24. doi: 10.18637/jss.v099.i03.

See Also

cold-class, cold, Methods

cold documentation built on Aug. 25, 2021, 5:06 p.m.