mdgc

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.

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References in zbMATH (referenced in 2 articles , 1 standard article )

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  1. Yuxuan Zhao, Madeleine Udell: gcimpute: A Package for Missing Data Imputation (2022) arXiv
  2. Benjamin Christoffersen, Mark Clements, Keith Humphreys, Hedvig Kjellström: Asymptotically Exact and Fast Gaussian Copula Models for Imputation of Mixed Data Types (2021) arXiv