synthpop: Bespoke Creation of Synthetic Data in R
By B. Nowok, G. Raab, Chris Dibben. 2016
451 citations recorded by the source corpus.
In many contexts, confidentiality constraints severely restrict access to unique and valuable microdata. Synthetic data which mimic the original observed data and preserve the relationships between variables but do not contain any disclosive records are one possible solution to this problem. The synthpop package for R, introduced in this paper, provides routines to generate synthetic versions of original data sets. We describe the methodology and its consequences for the data characteristics. We illustrate the package features using a survey data example.
Subfields: Artificial Intelligence, Management Science and Operations Research
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Citations in the corpus
- Small Sample Size Solutions (2020, methodology): “The syn function from the synthpop R package was used to generate the synthetic data (Nowok, Raab, and Dibben (2016).”
- Machine Learning for Synthetic Data Generation: a Review (2023, methodology): “Genomes Project and HGDP datasets synthpop [29] healthcare proper synthesis Statistical hypothesis testing SD2011 CycleGAN [30] vision GAN CNN pix2pix DP-CGAN [31] vision GAN deep CGAN MNIST BigGANs [32] vision GAN large scale GAN ImageNet VideoDiff [33] vision diffusion CNN BAIR Robot Pushing,…”
- The Role of Personality in COVID-19-Related Perceptions, Evaluations, and Behaviors: Findings Across Five Samples, Nine Traits, and 17 Criteria (2021, unknown): “The data sets underlying Studies 3–5, one exemplary synthetic data set (see Nowok et al., 2016) for each Study 1 and Study 2 (because these were not anonymous data sets), the analyses scripts/outputs including further analyses, and the overview of the administered items (including a translated…”