imputemulti 0.5.2

Implements imputation methods using EM and Data Augmentation for multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.

imputemulti

A Python library for multivariate multinomial data imputation using Expectation-Maximization (EM) and Data Augmentation (DA) algorithms, with a high-performance Rust core.

Features

• Multivariate multinomial imputation: Fill missing values in categorical datasets.
• Algorithms:
  • Expectation-Maximization (EM) algorithm.
  • Data Augmentation (DA) algorithm.
• Priors: Conjugate priors (Dirichlet) and data-dependent priors.
• Performance: High-performance Rust implementation for core counting and comparison functions.
  • Benchmarking: 10x - 100x faster than the original R/C++ implementation

Installation

• From Github: pip install git+https://github.com/alexwhitworth/pyimputeMulti.git
• From PyPI: pip install imputemulti

Usage

from imputemulti import multinomial_impute, load_tract2221

# Load example data
df = load_tract2221()

# Perform imputation
em_result = multinomial_impute(df, method="EM", conj_prior="none")
da_result = multinomial_impute(df, method="DA", conj_prior="none")

# Access imputed data
em_imputed_df = em_result.data[1]
da_imputed_df = da_result.data[1]

Detailed Examples

• See docs/
  • Basic Usage
  • Tutorial
  • Full Manual

References:

1. Schafer, Joseph L. Analysis of incomplete multivariate data. Chapter 7. CRC press, 1997.
2. Darnieder, William Francis. Bayesian methods for data-dependent priors. Diss. The Ohio State University, 2011.

Citation

If you use imputeMulti in your work, please cite the following:

@Manual{imputemulti_py,
    title = {{imputeMulti}: Imputation Methods for Multivariate Multinomial Data},
    author = {Alex Whitworth},
    year = {2021},
    howpublished = {\url{https://github.com/alexwhitworth/imputeMulti}},
    note         = {R package version 0.8.3; migrated to Python in 2026. Accessed: <Month DD, YYYY>}
}