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flexsynth

Flexible synthetic data for nested, longitudinal and linked multi-table data.

flexsynth generates utility-oriented synthetic data for supported flat, nested, longitudinal, and tree-linked structures, working natively in long format — no pivoting nested or longitudinal data to wide. It has first-class support for tree-linked multi-table data (e.g. patients → admissions → procedures / labs / meds), with referential integrity preserved by construction.

It ships two engines behind one interface:

Track A — high-utility (default). A sequential conditional-synthesis engine (CART, forest, conditional-inference trees, parametric methods) that models within-unit longitudinal dependence and cross-table relationships. No formal privacy guarantee; ships empirical utility and disclosure-risk diagnostics to inform review of a proposed release.
Track B — differentially private (opt-in). synth(..., privacy = dp_control(...)) implements a person-level (ε, δ) mechanism and emits its budget-accounting record. A governed release still requires independent review of the privacy unit, public-domain assumptions, contribution caps, and selected mechanism.

Synthetic data is not anonymisation, and Track A output must never be described as differentially private.

Features

Supported structures, native long format. Flat, nested / longitudinal (repeated visits), and tree-linked multi-table data — declared with a compact structure formula (~ id / visit / test).
Longitudinal dependence. Learned rows-per-unit count model, initial-state model, and a lag-1 Markov transition model, so within-unit autocorrelation across visits is preserved. Subject-invariant baseline columns are synthesised once per unit and broadcast.
Linked multi-table. Hierarchy inferred from keys; parent-first generation with foreign keys copied from the synthetic parent (referential integrity by construction), zero-inflated children-per-parent counts, and cross-table predictors. check_linkage() verifies key uniqueness and the absence of orphans.
Extensible methods. sample, cart, forest, ctree, norm, normrank built in; add your own with register_method().
Constraints / temporal logic. rule() enforces row-wise or per-unit constraints (e.g. dbp <= sbp, monotone length-of-stay) by unit-grain rejection sampling.
Pooled inference. pool_synth() / synth_glm() implement the published fully-synthetic variance rules (synthpop / Reiter) across m synthetic datasets. They use large-sample normal intervals; calibration still depends on the estimand, synthesis model, sample size, and analysis assumptions. compare_estimates() scores real-vs-synthetic analyses by confidence-interval overlap.
Diagnostics. diagnose() (marginal fit, correlation-matrix difference, categorical association via Cramer’s V, propensity pMSE) and disclosure_risk() (replicated uniques, distance-to-closest-record, membership-inference AUC, and TCAP attribute-disclosure via target =).
Performance. Optional data.table fast-path and parallel replicates (synth_control(parallel = TRUE)) with reproducible L’Ecuyer streams.

Installation

# install.packages("remotes")
remotes::install_github("lauyeehow1986-hub/Flexsynth")

The package installs with base-R dependencies. The default method = "cart" requires the suggested rpart package; ranger / partykit unlock additional tree methods, and data.table unlocks the row-binding fast-path. Use method = "sample" when only base R is available.

Quick start

Single nested / longitudinal table

library(flexsynth)

df <- data.frame(
  id    = rep(1:20, each = 2),
  visit = rep(1:2, times = 20),
  age   = rep(round(rnorm(20, 60, 8)), each = 2),
  sbp   = round(rnorm(40, 130, 15))
)

res <- synth(df, structure = ~ id / visit, method = "cart", seed = 1)
head(as.data.frame(res))

Multiple linked tables, synthesised jointly

patients <- data.frame(id = 1:50, sex = sample(c("F", "M"), 50, TRUE))
adm <- do.call(rbind, lapply(patients$id, function(pid) {
  n <- 1 + rpois(1, 0.6)
  data.frame(id = pid, admission_id = seq_len(n), los = 1L + rpois(n, 3))
}))

res <- synth_linked(
  tables     = list(patients = patients, admissions = adm),
  structures = list(patients   = ~ id,
                    admissions = ~ id / admission_id),
  keys       = list(patients   = "id",
                    admissions = c("id", "admission_id")),
  seed = 1
)
syn <- as.list(res)
check_linkage(syn, keys = list(patients = "id",
                               admissions = c("id", "admission_id")))

Diagnostics and disclosure risk

syn <- as.data.frame(synth(df, structure = ~ id / visit, seed = 1))
analysis_vars <- c("age", "sbp")             # exclude generated structure keys
d <- diagnose(real = df, syn = syn, vars = analysis_vars)
plot(d)                                  # overlaid marginals
disclosure_risk(real = df, syn = syn, quasi = analysis_vars)

Pooled inference from synthetic data

# Analyse all m synthetic sets with a published fully-synthetic pooling rule.
# A single set analysed naively generally under-states synthesis uncertainty.
res <- synth(df, structure = ~ id / visit, m = 10, seed = 1)
synth_glm(res, sbp ~ age)                 # pooled linear model
# any estimator works via pool_synth(res, function(d) <fit returning coef/vcov>)

Opt into differential privacy (Track B)

dp <- dp_control(epsilon = 1, delta = 1e-6, mechanism = "gaussian",
                 bounds = list(visit = c(1, 2), age = c(18, 100), sbp = c(60, 240)))
dp_res <- synth(df, structure = ~ id, privacy = dp, seed = 1)
dp_res$privacy                           # the (ε, δ) accounting record

Track B supports three release shapes:

a flat table (~ id);
a longitudinal table (~ id / visit) using a bounded DP Markov model; and
a tree-linked hierarchy via synth_linked(), using the root entity as the privacy unit.

dp_control() also exposes privately learned domains, cross-table conditioning, higher-order transitions, adaptive marginal selection, Private-PGM reconciliation, and AIM-style models. These controls trade statistical fidelity, cell sparsity, runtime, and privacy budget; they are not universally beneficial.

A governed DP release requires more than setting epsilon: define the privacy unit and public domain assumptions, justify contribution caps, retain the accounting record, review utility at the intended analysis grain, and obtain independent privacy review. See vignette("differential-privacy") for the supported combinations, accounting model, and limitations.

Learn more

vignette("getting-started") — getting started
vignette("valid-inference") — pooled inference and attribute-disclosure (TCAP)
vignette("nested-longitudinal") — repeated-measures data
vignette("linked-cardiac") — multi-table linked synthesis
vignette("differential-privacy") — Track B
docs/roadmap.md — phased delivery and what’s next

Data & privacy

Bundled example datasets are fully synthetic cardiac data (see data-raw/make_toy_cardiac.R). No real patient data ships with this package, and none should ever be committed.

License

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.