Journal
JOURNAL OF STATISTICAL SOFTWARE
Volume 77, Issue 8, Pages 1-26Publisher
JOURNAL STATISTICAL SOFTWARE
DOI: 10.18637/jss.v077.i08
Keywords
beta regression; Gaussian copula; longitudinal data; marginal regression; multi-variate probit; R; spatial regression
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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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