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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "PhysioML" in publications use:'
type: software
license: MIT
title: 'PhysioML: Leakage-Aware Machine Learning for Physiological Time Series'
version: 0.4.2
doi: 10.32614/CRAN.package.PhysioML
identifiers:
- type: url
  value: https://x-biosignal.github.io/PhysioML/
abstract: Provides leakage-aware, reproducible feature transforms and reference deep-learning
  workflows for physiological time series stored in PhysioExperiment objects. Canonical
  catch22 characteristics are computed through the official R backend, while ROCKET
  and MiniRocket transforms use guarded optional Python backends. Guarded optional
  torch and luz backends provide case-aware datasets, compact neural architectures,
  training, and prediction. Explicit contracts preserve case, channel, feature, normalization,
  backend, and model provenance without claiming clinical validation. Named transfer-learning
  policies, leakage-safe CORAL alignment, and immutable hash-verified foundation-model
  caches provide bounded reuse across domains.
authors:
- family-names: Matsui
  given-names: Yusuke
  email: mail.to.matsui@gmail.com
repository: https://x-biosignal.r-universe.dev
repository-code: https://github.com/x-biosignal/PhysioML
commit: 9d10f45581685a9592b0846fe7d16ff93741ad30
url: https://x-biosignal.r-universe.dev/PhysioML
date-released: '2026-10-03'
contact:
- family-names: Matsui
  given-names: Yusuke
  email: mail.to.matsui@gmail.com