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Migrating from synthpop

Why switch?

synthpop excels at disclosure-controlled individual-level microdata but lacks joint distribution modeling via copulas. rsdv uses a Gaussian copula to preserve inter-column correlations.

Side-by-side comparison

synthpop workflow

library(synthpop)
synth <- syn(adult_income[, c("age", "occupation", "income")])
synthetic_data <- synth$syn

rsdv workflow

library(rsdv)

set.seed(42)

meta  <- metadata(adult_income) |>
  set_column_type("age",        "numerical") |>
  set_column_type("occupation", "categorical") |>
  set_column_type("income",     "categorical")

syn   <- gaussian_copula_synthesizer(meta)
syn   <- fit(syn, adult_income)
synthetic_data <- sample(syn, n = nrow(adult_income))

Key differences

Feature	synthpop	rsdv
Correlation modeling	CART-based sequential	Gaussian copula over all column types
Column constraints	Limited	Equality, inequality, fixed combos, custom
Conditional sampling	Via predictor order	`sample_conditions()` on categorical values
Quality metrics	Built-in utility measures	KS, TVD, correlation & contingency similarity, ML efficacy
Diagnostics	None	Validity report (ranges, categories, key uniqueness)
Privacy metrics	None	NNDR, attribute disclosure risk
Python interop	No	API-compatible with SDV

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.
Health stats visible at Monitor.