Synthetic Microdata Generator
Overview
This tool fits a non-parametric Bayesian model called a "hierarchically coupled mixture model with local dependence (HCMM-LD)" to the original microdata in order to generate synthetic microdata for privacy protection. The non-parametric feature of the adopted model is useful for capturing the joint distribution of the original input data in a highly flexible manner, leading to the generation of synthetic data whose distributional features are similar to that of the input data. The package allows the original input data to have missing values and impute them with the posterior predictive distribution, so no missing values exist in the synthetic data output. The method builds on the work of Murray and Reiter (2016) doi:10.1080/01621459.2016.1174132.
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 7
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 67%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- Help pages citing references
- 29%
Downloads
Dependencies
Installing synMicrodata adds 2 packages (2 with compiled code, 2.8 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppArmadillo 1.0 MB.
Code & Tests
People & History
Previously maintained by Hang J. Kim (1.0.0 to 2.1.0).
Package metadata
- First published
- 2023-02-06
- Total releases
- 6 / 3 yrs
- License
- GPL (>= 3) OSI
- Download size
- 19 KB
Cite
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