swMATH ID: 41457
Software Authors: Yuxuan Zhao, Madeleine Udell
Description: gcimpute: A Package for Missing Data Imputation. This article introduces the Python package gcimpute for missing data imputation. gcimpute can impute missing data with many different variable types, including continuous, binary, ordinal, count, and truncated values, by modeling data as samples from a Gaussian copula model. This semiparametric model learns the marginal distribution of each variable to match the empirical distribution, yet describes the interactions between variables with a joint Gaussian that enables fast inference, imputation with confidence intervals, and multiple imputation. The package also provides specialized extensions to handle large datasets (with complexity linear in the number of observations) and streaming datasets (with online imputation). This article describes the underlying methodology and demonstrates how to use the software package.
Homepage: https://arxiv.org/abs/2203.05089
Source Code:  https://github.com/udellgroup/gcimputeR
Dependencies: Python
Keywords: arXiv_stat.ME; arXiv_stat.AP; gcimpute; Python; missing data; single imputation; multiple imputation; Gaussian copula; mixed data; imputation uncertainty
Related Software: gcimputeR; Scikit; CoImp; mdgc; sbgcop; softImpute; missForest; MICE; R; missMDA; Amelia; Python
Cited in: 0 Publications

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gcimpute: A Package for Missing Data Imputation
Yuxuan Zhao, Madeleine Udell