Description Details Author(s) References Examples
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.
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.
Peter Hoff <peter.hofff@duke.edu>
Hoff (2007) “Extending the rank likelihood for semiparametric copula estimation”
1 2 3 | fit<-sbgcop.mcmc(swiss)
summary(fit)
plot(fit)
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