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mdgc  

Missing Data Imputation Using Gaussian Copulas
View on CRAN: Click here


Download and install mdgc package within the R console
Install from CRAN:
install.packages("mdgc")

Install from Github:
library("remotes")
install_github("cran/mdgc")

Install by package version:
library("remotes")
install_version("mdgc", "0.1.7")



Attach the package and use:
library("mdgc")
Maintained by
Benjamin Christoffersen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-02-17
Latest Update: 2023-05-04
Description:
Provides functions to impute missing values using Gaussian copulas for mixed data types as described by Christoffersen et al. (2021) . The method is related to Hoff (2007) and Zhao and Udell (2019) 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) in addition to also support multinomial variables.
How to cite:
Benjamin Christoffersen (2021). mdgc: Missing Data Imputation Using Gaussian Copulas. R package version 0.1.7, https://cran.r-project.org/web/packages/mdgc. Accessed 05 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:29), 0.1.1 (2021-02-17 11:20), 0.1.2 (2021-02-26 22:40), 0.1.3 (2021-03-04 08:50), 0.1.4 (2021-03-12 22:50), 0.1.5 (2021-06-14 09:50), 0.1.6 (2022-09-10 20:22)
Other packages that cited mdgc R package
View mdgc citation profile
Other R packages that mdgc depends, imports, suggests or enhances
Complete documentation for mdgc
Functions, R codes and Examples using the mdgc R package
Some associated functions: get_mdgc . get_mdgc_log_ml . mdgc-package . mdgc . mdgc_fit . mdgc_impute . mdgc_log_ml . mdgc_start_value . 
Some associated R codes: RcppExports.R . catch-routine-registration.R . mdgc-package.R . mdgc.R .  Full mdgc package functions and examples
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