sbgcop: Semiparametric Bayesian Gaussian copula estimation
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.
Version: 0.95
Date: 2007-03-09
Author: Peter Hoff
Maintainer: Peter Hoff <hoff at stat.washington.edu>
License: GPL Version 2 or later
URL: http://www.stat.washington.edu/hoff
CRAN checks: sbgcop results
Downloads:
Package source: sbgcop_0.95.tar.gz
MacOS X binary: sbgcop_0.95.tgz
Windows binary: sbgcop_0.95.zip
Reference manual: sbgcop.pdf
sbgcop: Semiparametric Bayesian Gaussian copula estimation
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.
Version: 0.95
Date: 2007-03-09
Author: Peter Hoff
Maintainer: Peter Hoff <hoff at stat.washington.edu>
License: GPL Version 2 or later
URL: http://www.stat.washington.edu/hoff
CRAN checks: sbgcop results
Downloads:
Windows binary: sbgcop_0.95.zip
sbgcop: Semiparametric Bayesian Gaussian copula estimation
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.
Version: 0.95
Date: 2007-03-09
Author: Peter Hoff
Maintainer: Peter Hoff <hoff at stat.washington.edu>
License: GPL Version 2 or later
URL: http://www.stat.washington.edu/hoff
CRAN checks: sbgcop results
Downloads:
Reference manual: sbgcop.pdf
Creates a Gaussian mixture model with specified architecture.MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space
DIM, the number of centres in the mixture model and the type of the
mixture model, and returns a data structure MIX.
Generate the digital AWGN signal n[k] (sampled n(t)) by generating zero mean
Gaussian random variables independently (separately) for each k MATLAB function random.
