robosynth-package: robosynth: Robust synthetic data generation with...

In simongrund1/robosynth: Robust Synthetic Data Generation with Data-Augmented Multiple Imputation

robosynth: Robust synthetic data generation with data-augmented multiple imputation

Description

Provides methods for synthetic data generation using the data-augmented multiple imputation (DA-MI) approach by Jiang et al. (2021) and tools for handling and analyzing synthetic data.

The DA-MI approach combines masking and imputation procedures by including masked copies of the original data in the synthesis model. This package provides functions for creating masked copies of different variables types (mask.continuous and mask.categorical), specifying the synthesis model (synthesis.model, masking.model, and combine.models), generating synthetic data (synthesize), and analyzing the synthetic data (e.g., with, pool).

Author(s)

Authors: Simon Grund, Alexander Robitzsch, Oliver Luedtke

References

Jiang, B., Raftery, A. E., Steele, R. J., & Wang, N. (2021). Balancing inferential integrity and disclosure risk via model targeted masking and multiple imputation. Journal of the American Statistical Association. Advance online publication. doi: 10.1080/01621459.2021.1909597

simongrund1/robosynth documentation built on March 20, 2022, 6:15 p.m.