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SynthesizerPlus

Synthetic data generation for R with copulas, built-in quality and disclosure-risk evaluation, ggplot2 visualisation and multi-format I/O.

SynthesizerPlus learns the marginal distribution of every column and the dependence between columns (Gaussian or Student-t copula) from a real data set, and generates new records with the same statistical structure. It handles numeric, integer, categorical, ordinal, logical, date and date-time columns, missing values and their patterns, stratified models, and univariate or multivariate time series.

Installation

# development version
remotes::install_github("bogdanoancea/SynthesizerPlus", build_vignettes = TRUE)

Optional packages enable extra file formats: arrow or nanoparquet (Parquet), arrow (Feather), hdf5r (HDF5), jsonlite (JSON/NDJSON), readxl with writexl or openxlsx (Excel), haven (SPSS, Stata, SAS), fst, and data.table (fast CSV).

Quick start

library(SynthesizerPlus)

fit <- fit_synthesizer(airquality, by = "Month")   # learn
syn <- generate(fit, n = 1000, seed = 1)           # generate

cmp <- compare_synthetic(airquality, syn, seed = 1) # evaluate
cmp
plot(cmp, type = "marginals")
plot(cmp, type = "association")

write_data(syn, "synthetic.parquet")               # deliver

Features

Area	Functions
Synthesis	`fit_synthesizer()`, `generate()`, `synthesize()`, `simulate()`
Disclosure control	`disclosure_risk()` (replicated uniques, correct attribution probability), `dcr()`, simulated `census_sim` data
Closeness control	`generate(closeness = )`, `perturbation()`, `calibrate_closeness()`
ML data augmentation	`augment_data()`, `ts_windows()`
Time series	`fit_synthesizer()` on `ts`: copula-VAR, block and stationary bootstrap
Large data	`generate_to_file()` (chunked, CSV / Parquet / HDF5 / …)
Multivariate distributions	`r_mvnorm()`, `r_mvt()`, `r_mvlnorm()`, `r_mvskewnorm()`, `r_dirichlet()`, `r_mvmixture()`, `r_copula()` (Gaussian, t, Clayton, Gumbel, Frank), `r_mvdist()`, `make_corr()`
Evaluation	`compare_synthetic()`, `marginal_metrics()`, `association_matrix()`, `pmse()`, `discriminator_auc()`, `ci_overlap()`, `dcr()`
Visualisation	`plot_marginals()`, `plot_categorical()`, `plot_association()`, `plot_pairs()`, `plot_qq()`, `plot_ts()`, `plot_acf()`, `plot_dcr()`, `autoplot()`
I/O	`read_data()`, `write_data()`, `supported_formats()`, `match_types()`

Supported file formats: CSV, TSV (optionally .gz/.bz2/.xz), RDS, RData, Parquet, Feather/Arrow IPC, HDF5, JSON, NDJSON, Excel, SPSS, Stata, SAS (sas7bdat read, xpt read/write) and fst.

Example scripts

Runnable scripts are installed with the package:

ex <- function(f) system.file("examples", f, package = "SynthesizerPlus")
source(ex("demo.R"), echo = TRUE)          # a tour of the whole package
source(ex("closeness.R"), echo = TRUE)     # closeness, perturbations, calibration
source(ex("privacy.R"), echo = TRUE)       # releasing confidential microdata
source(ex("augmentation.R"), echo = TRUE)  # more training data for ML models

Vignettes

Getting started — the model, column types, missing data, strata, dependence control
Evaluating and visualising synthetic data — fidelity, utility and disclosure risk
Releasing confidential microdata as synthetic data
Controlling how close synthetic data are to the real data
Augmenting small training sets for machine learning
Regularising high-variance models with synthetic data
Synthetic time series
Generating data from multivariate distributions
Reading and writing data: CSV, Parquet, HDF5 and more

Acknowledgement

The idea of combining empirical quantile functions with a Gaussian copula follows the synthesizer package by Mark van der Loo. SynthesizerPlus is an independent implementation.

License

EUPL (European Union Public Licence)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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