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

Install the current CRAN release:

install.packages("SynthesizerPlus")

# 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

Conditional synthesis of one target (experimental)

The copula represents each variable by one latent coordinate and all dependence by correlations. When an analysis centres on one response whose relation to the other columns is nonlinear, or on an unordered categorical response, that response can instead be drawn from a conditional model given the copula-generated predictors:

# continuous response: CART (default), penalised GAM, or automatic choice
set.seed(1)
d <- data.frame(x = runif(600, -2, 2), z = rnorm(600))
d$y <- sin(3 * d$x) + 0.5 * d$z + rnorm(600, sd = 0.2 + 0.2 * abs(d$x))

fit_cart <- fit_synthesizer(d, nonlinear_target = "y", seed = 10)
fit_gam  <- fit_synthesizer(d, nonlinear_target = "y", nonlinear_engine = "gam", seed = 10)
fit_auto <- fit_synthesizer(d, nonlinear_target = "y", nonlinear_engine = "auto", seed = 10)
fit_auto$pooled$nonlinear$selection$scores   # cross-validated scores
syn <- generate(fit_auto, n = 1000, seed = 11)

# unordered categorical response: multinomial model with spline terms
fit_nom <- fit_synthesizer(iris, nominal_target = "Species", seed = 42)
syn_nom <- generate(fit_nom, n = 500, seed = 43)

• Continuous response (nonlinear_target, needs rpart or mgcv): a CART or GAM conditional mean plus resampled residuals, calibrated to the observed marginal of the response. nonlinear_engine = "auto" chooses the engine by training-only cross-validation of the conditional predictive distribution (CRPS).
• Unordered categorical response (nominal_target, needs nnet): a multinomial logit with natural-spline terms for numeric predictors (kept when they improve the AIC over linear terms), so category probabilities can depend nonlinearly on them.
• Only one target per fit; non-default closeness, dependence and perturb are not supported for such fits. Strata too small for the conditional model fall back to their copula, with a warning.
• The fitted conditional models contain training-data information and must not be released as if privacy-safe. Cross-validated or held-out accuracy does not certify synthetic-data quality or disclosure safety: use the evaluation and disclosure-risk tools, and inst/examples/hybrid_nonlinear_benchmark.R for paired comparisons on held-out data.

See vignette("hybrid-nonlinear") and vignette("nominal-synthesis").

Features

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
• Hybrid copula and conditional CART/GAM synthesis for nonlinear relationships (experimental)
• Nominal conditional synthesis (experimental)

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)