cold: An R Package for the Analysis of Count Longitudinal Data
Dublin Core
Title
cold: An R Package for the Analysis of Count Longitudinal Data
Subject
count longitudinal data, exact likelihood, Markov chain, marginal models, random
effects models
effects models
Description
This paper describes the R package cold for the analysis of count longitudinal data. In
this package marginal and random effects models are considered. In both cases estimation
is via maximization of the exact likelihood and serial dependence among observations is
assumed to be of Markovian type and referred as the integer-valued autoregressive of
order one process. For random effects models adaptive Gaussian quadrature and Monte
Carlo methods are used to compute integrals whose dimension depends on the structure
of random effects. cold is written partly in R language, partly in Fortran 77, interfaced
through R and is built following the S4 formulation of R methods
this package marginal and random effects models are considered. In both cases estimation
is via maximization of the exact likelihood and serial dependence among observations is
assumed to be of Markovian type and referred as the integer-valued autoregressive of
order one process. For random effects models adaptive Gaussian quadrature and Monte
Carlo methods are used to compute integrals whose dimension depends on the structure
of random effects. cold is written partly in R language, partly in Fortran 77, interfaced
through R and is built following the S4 formulation of R methods
Creator
M. Helena Gonçalves
Source
https://www.jstatsoft.org/article/view/v099i03
Publisher
Universidade do Algarve
Date
August 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
M. Helena Gonçalves, “cold: An R Package for the Analysis of Count Longitudinal Data,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8204.