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<html><head><title>R: Semiparametric Bayesian Gaussian copula estimation</title>
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<table width="100%" summary="page for sbgcop-package {sbgcop}"><tr><td>sbgcop-package {sbgcop}</td><td align="right">R Documentation</td></tr></table>
<h2>Semiparametric Bayesian Gaussian copula estimation</h2>
<h3>Description</h3>
<p>
This package estimates parameters of a Gaussian
copula, treating the univariate marginal distributions
as nuisance parameters as described in Hoff(2007). It also
provides a semiparametric imputation procedure for missing
multivariate data.
</p>
<h3>Details</h3>
<p>
<table summary="Rd table">
<tr>
<td align="left">Package: </td> <td align="left"> sbgcop</td>
</tr>
<tr>
<td align="left"> Type: </td> <td align="left"> Package</td>
</tr>
<tr>
<td align="left"> Version: </td> <td align="left"> 0.95 </td>
</tr>
<tr>
<td align="left"> Date: </td> <td align="left"> 2007-03-09</td>
</tr>
<tr>
<td align="left"> License: </td> <td align="left"> GPL Version 2 or later </td>
</tr>
</table>
<p>
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.
</p>
<h3>Author(s)</h3>
<p>
Peter Hoff <hoff@stat.washington.edu>
</p>
<h3>References</h3>
<p>
Hoff (2007) ``Extending the rank likelihood for semiparametric copula estimation''
</p>
<h3>Examples</h3>
<pre>
fit<-sbgcop.mcmc(swiss)
summary(fit)
plot(fit)
</pre>
<hr><div align="center">[Package <em>sbgcop</em> version 0.95 <a href="00Index.html">Index]</a></div>
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