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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.