mdgc: Missing Data Imputation Using Gaussian Copulas

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.

Version: 0.1.2
Depends: R (≥ 3.5.0)
Imports: Rcpp
LinkingTo: Rcpp, RcppArmadillo, testthat, BH, psqn
Suggests: testthat, catdata
Published: 2021-02-26
Author: Benjamin Christoffersen ORCID iD [cre, aut], Alan Genz [cph], Frank Bretz [cph], Torsten Hothorn [cph], R-core [cph], Ross Ihaka [cph]
Maintainer: Benjamin Christoffersen <boennecd at gmail.com>
BugReports: https://github.com/boennecd/mdgc/issues
License: GPL-2
URL: https://github.com/boennecd/mdgc
NeedsCompilation: yes
SystemRequirements: C++14
CRAN checks: mdgc results

Downloads:

Reference manual: mdgc.pdf
Package source: mdgc_0.1.2.tar.gz
Windows binaries: r-devel: mdgc_0.1.1.zip, r-release: not available, r-oldrel: not available
macOS binaries: r-release: mdgc_0.1.1.tgz, r-oldrel: mdgc_0.1.1.tgz
Old sources: mdgc archive

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