This article describes the R package gcmr for fitting Gaussian copula marginal regression models. The Gaussian copula provides a mathematically convenient framework to handle various forms of dependence in regression models arising, for example, in time series, longitudinal studies or spatial data. The package gcmr implements maximum likelihood inference for Gaussian copula marginal regression. The likelihood function is approximated with a sequential importance sampling algorithm in the discrete case. The package is designed to allow a flexible specification of the regression model and the dependence structure. Illustrations include negative binomial modeling of longitudinal count data, beta regression for time series of rates and logistic regression for spatially correlated binomial data.

Gaussian copula regression in R

VARIN, Cristiano
2017

Abstract

This article describes the R package gcmr for fitting Gaussian copula marginal regression models. The Gaussian copula provides a mathematically convenient framework to handle various forms of dependence in regression models arising, for example, in time series, longitudinal studies or spatial data. The package gcmr implements maximum likelihood inference for Gaussian copula marginal regression. The likelihood function is approximated with a sequential importance sampling algorithm in the discrete case. The package is designed to allow a flexible specification of the regression model and the dependence structure. Illustrations include negative binomial modeling of longitudinal count data, beta regression for time series of rates and logistic regression for spatially correlated binomial data.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3663990
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