Synthetic datasets for geometric smoothing

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geosmooth represents a synthetic dataset as four serializable components: geometry, sampling, truth, and response. synthetic.spec() combines those components without drawing data. materialize.synthetic() performs all random work under an explicit seed and RNG policy and returns a validated synthetic_dataset.

library(geosmooth)

circle.spec <- synthetic.spec(
  geometry = synthetic.circle(radius = 1, ambient.dim = 2),
  sampling = synthetic.sampling.grid.interval(
    0, 2 * pi, endpoints = "exclude.lower"),
  truth = synthetic.truth.polynomial(c(b0 = 0)),
  response = synthetic.response.gaussian(sd = 0.05)
)
circle.data <- materialize.synthetic(circle.spec, n = 80, seed = 2026)
circle.data
#> <synthetic_dataset> adhoc__specd155e302ac4d6d54d5298f321d3b01cbba0f539a0eaca5e892e69502f3fce186__n80__seed2026__rngnamed.stream.v1
#>   n=80  intrinsic=1  ambient=2  truth=population
plot(circle.data)

Quadforms use separate intrinsic and ambient dimensions. A flat geometry has no quadratic forms; a paraboloid or saddle supplies one or more symmetric forms.

surface.spec <- synthetic.spec(
  geometry = synthetic.quadform(
    intrinsic.dim = 2,
    ambient.dim = 5,
    forms = list(diag(c(1, -1))),
    frame = "random.orthonormal"
  ),
  sampling = synthetic.sampling.uniform.box(-1, 1),
  truth = synthetic.truth.polynomial(c(b0 = 0, b1 = 1, b22 = -0.5)),
  response = synthetic.response.gaussian(sd = 0.1)
)
surface.data <- materialize.synthetic(surface.spec, n = 100, seed = 17)
as.data.frame(surface.data)
#>                                                                                                      dataset.id
#> 1 adhoc__spec90adf9b19cf8db3041878a96a32d5b64aefa1f3bcd1b727a58bb377520b71d2f__n100__seed17__rngnamed.stream.v1
#>   recipe.id registry.tag   n intrinsic.dim ambient.dim truth.scope
#> 1      <NA>         <NA> 100             2           5  population
#>        rng.policy
#> 1 named.stream.v1
#>                                                           checksum
#> 1 87ba1bb4a26245e58e0edd263233182f4acd37e4d44c53dcc3a01e7687a3b9c6

Maintained benchmark recipes are discoverable through synthetic.registry.ids(). G1–G7 remain compatibility identifiers; the S01–S16 and V1–V3 identifiers preserve the shared SSRHE regression suite. Ordinary datasets bind their full specification hash into dataset.id.

head(synthetic.registry.ids())
#> [1] "G1"  "G2"  "G3a" "G3b" "G3c" "G3d"
registered <- synthetic.registry.spec("S01.V1")
registered.data <- materialize.synthetic(
  registered, n = 160,
  seed = synthetic.registry.seed("S01.V1", replicate = 1),
  rng.policy = "legacy"
)
synthetic.dataset.checksum(registered.data)
#> [1] "b157f04855125d66efa3802655da6ba495dd9655ea44becc1076daf76c5c5f5c"

Use rng.policy = "named.stream.v1" for new work. The "legacy" policy exists for frozen scientific parity and pins historical RNG behavior. Predictor noise, response noise, and finite-design truth normalization are distinct contracts; they should not be substituted for one another.

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.
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