sbgcop-package: Semiparametric Bayesian Gaussian Copula Estimation and...

Description Details Author(s) References Examples

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 <[email protected]>

References

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

Examples

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

pdhoff/sbgcop documentation built on May 31, 2019, 12:12 p.m.