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| Authors: | Guido Masarotto, Cristiano Varin | ||||
| Title: | Gaussian Copula Regression in R | ||||
| 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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Page views:: 2526. Submitted: 2015-06-16. Published: 2017-04-29. |
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| Paper: |
Gaussian Copula Regression in R
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| DOI: |
10.18637/jss.v077.i08
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This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license. |