mdgc-package

_PACKAGE

The mdgc package is used to estimate Gaussian Copula models for mixed data
types (continuous, binary, ordinal, and multinomial) that can be used for
imputations. The main function is the <code><a rd-options="" href="/link/mdgc?package=mdgc&version=0.1.2" data-mini-rdoc="mdgc::mdgc">mdgc</a></code> function. The rest
of the functions in the package give the user access to lower level
functions.
Examples are provided at <a href="https://github.com/boennecd/mdgc">https://github.com/boennecd/mdgc</a>. The package
is still in a development stage and the API may change.

Provides functions to impute missing values using Gaussian
copulas for mixed data types as described by Christoffersen et al.
(2021) <arXiv:2102.02642>. The method is related to Hoff (2007)
<doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845>
but differs by making a direct approximation of the log marginal likelihood
using an extended version of the Fortran code created by Genz and Bretz
(2002) <doi:10.1198/106186002394> in addition to also support multinomial
variables.

Benjamin Christoffersen

mdgc

Missing Data Imputation Using Gaussian Copulas

Alan Genz

Frank Bretz

Torsten Hothorn

R-core 

Ross Ihaka

mdgc-package function

The mdgc package is used to estimate Gaussian Copula models for mixed data
types (continuous, binary, ordinal, and multinomial) that can be used for
imputations. The main function is the <code><a rd-options='' href='mdgc'>mdgc</a></code> function. The rest
of the functions in the package give the user access to lower level
functions.
Examples are provided at <a href='https://github.com/boennecd/mdgc'>https://github.com/boennecd/mdgc</a>. The package
is still in a development stage and the API may change.

mdgc: Missing Data imputation using Gaussian Copulas — mdgc-package

mdgc: Missing Data imputation using Gaussian Copulas

mdgc-package: mdgc: Missing Data imputation using Gaussian Copulas

Description

Arguments

References

See Also