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mdgc (version 0.1.2)

Missing Data Imputation Using Gaussian Copulas

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.

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Install

install.packages('mdgc')

Monthly Downloads

182

Version

0.1.2

License

GPL-2

Issues

0

Pull Requests

0

Stars

10

Forks

2

Maintainer

Benjamin Christoffersen

Last Published

February 26th, 2021

Functions in mdgc (0.1.2)

get_mdgc

Get mdgc Object
mdgc_fit

Estimate the Model Parameters
get_mdgc_log_ml

Get Pointer to C++ Object to Approximate the Log Marginal Likelihood
mdgc-package

mdgc: Missing Data imputation using Gaussian Copulas
mdgc

Perform Model Estimation and Imputation
mdgc_start_value

Get Starting Value for the Covariance Matrix Using a Heuristic
mdgc_log_ml

Evaluate the Log Marginal Likelihood and Its Derivatives
mdgc_impute

Impute Missing Values