A grouped hyperspectral workflow

hypermodR brings hyperspectral data, train-only preprocessing, grouped modelling and explicit evaluation into one R workflow. It is development software for research. Executable interfaces, technical qualification and scientific validity are separate states. No clinical performance or general instrument compatibility is claimed.

The synthetic regression uses subjects as independent units. All bands are measured synthetic coordinates in nm; radiometry is arbitrary. No biological or device inference follows from these values. One outer fold is illustrated; all model selection belongs inside outer training.

Route	Current scope	Qualification boundary
R models	Grouped statistical, chemometric and conventional learning adapters	Synthetic tests; optional R engines require their packages
Data bridges	S4 datasets/sequences, S3 source conversion and bounded blocks	Synthetic and companion fixtures; camera profiles remain unqualified
Neural models	Package-owned trainable references and named upstream admission contracts	No named pretrained model scientifically qualified
Runtime setup	Explicit resolver, reviewed manifest, isolated installation and owned workers	Automated resolver: Linux x86_64 only; exact live smoke/parity required
Workbench	Local Shiny workflow using the same package API	Browser/accessibility qualification tracked separately

data <- hm_example(seed = 42L)
if (!isTRUE(all(
  methods::validObject(data), nrow(data@observations) == 96L,
  identical(data@bands$wavelength, seq(500, 850, length.out = 8L))
))) {
  cli::cli_abort("Example assertion failed.", class = "hm_example_error")
}
task <- hm_task("response", "regression", "spectrum", "subject_id")
splits <- hm_split(data, task, folds = 3L, inner_folds = 2L, seed = 7L)
fold <- splits$folds[[1L]]
if (!isTRUE(all(!length(intersect(
  data@observations$subject_id[fold$train],
  data@observations$subject_id[fold$test]
))))) {
  cli::cli_abort("Example assertion failed.", class = "hm_example_error")
}
workflow <- hm_workflow(
  task, hm_recipe(list(list(type = "scale"))),
  hm_model_spec("lm", task)
)
fit <- hm_fit(workflow, data, fold)
heldout <- hm_spectra(data, list(
  observation_id = data@observations$observation_id[fold$test]
))
prediction <- hm_predict(fit, heldout)
evaluation <- hm_evaluate(prediction, heldout$observations)
errors <- prediction$values - heldout$observations$response
expected_rmse <- sqrt(mean(errors^2))
if (!isTRUE(all(isTRUE(all.equal(unname(evaluation$metrics["rmse"]),
                                 expected_rmse))))) {
  cli::cli_abort("Example assertion failed.", class = "hm_example_error")
}
evaluation
#>       rmse        mae       bias 
#> 0.10558376 0.08430486 0.03453047
uncertainty <- hm_bootstrap(evaluation, unit = "subject_id", draws = 20L,
                            seed = 9L)
if (!isTRUE(all(uncertainty$invalid_draws == 0L))) {
  cli::cli_abort("Example assertion failed.", class = "hm_example_error")
}
uncertainty$conditioning
#> [1] "fixed predictions; fitting and selection uncertainty excluded"
run <- tempfile("hypermodR-run-")
hm_save_run(list(
  fit = fit, prediction = prediction, evaluation = evaluation,
  uncertainty = uncertainty
), run)
restored <- hm_read_run(run)
if (!isTRUE(all(identical(restored$prediction, prediction)))) {
  cli::cli_abort("Example assertion failed.", class = "hm_example_error")
}
unlink(run, recursive = TRUE)

hm_bootstrap(evaluation, unit = "subject_id", draws = 20L) resamples whole held-out units and reports conditional uncertainty. Such small demonstration budgets are not precision recommendations. hm_compare() pairs identical support; it does not turn a zero-containing interval into a significance claim.