README.md

In SynthesizerPlus: Synthetic Data Generation with Copulas, Evaluation and Multi-Format I/O

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)

SynthesizerPlus documentation built on Oct. 16, 2026, 5:10 p.m.