sbgcop-package {sbgcop}

Semiparametric Bayesian Gaussian Copula Estimation and Imputation

Description

Estimation and inference for parameters in a Gaussian copula model, treating univariate marginal distributions as nuisance parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. This pacakge also provides a semiparametric imputation procedure for missing multivariate data.

Details

Package:	sbgcop
Type:	Package
Version:	0.980
Date:	2018-05-25
License:	GPL Version 2 or later

This function produces MCMC samples from the posterior distribution of a correlation matrix, using a scaled inverse-Wishart prior distribution and an extended rank likelihood. It also provides imputation for missing values in a multivariate dataset.

Author(s)

Peter Hoff <[e-mail]>

References

Hoff (2007) “Extending the rank likelihood for semiparametric copula estimation”

Examples

fit<-sbgcop.mcmc(swiss)
summary(fit)
plot(fit)