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: |
0.1.6 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rcpp |
| LinkingTo: |
Rcpp, RcppArmadillo, testthat, BH, psqn |
| Suggests: |
testthat, catdata |
| Published: |
2022-09-10 |
| Author: |
Benjamin Christoffersen
 [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 |
| Materials: |
NEWS |
| In views: |
MissingData |
| CRAN checks: |
mdgc results |
Documentation:
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