gcimpute: A Package for Missing Data Imputation

Yuxuan Zhao

doi:10.18637/jss.v108.i04

gcimpute: A Package for Missing Data Imputation

2024-01-01

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AI

DOI：10.18637/jss.v108.i04

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This article introduces the Python package gcimpute for missing data imputation. Package 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.

Keyword:

missing data

single imputation

multiple imputation

Gaussian copula

mixed data

imputation uncertainty

Python

gcimpute: A Package for Missing Data Imputation

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