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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: MEDS-Transforms
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - family-names: McDermott
    given-names: Matthew
    email: mattmcdermott8@gmail.com
    orcid: 'https://orcid.org/0000-0001-6048-9707'
  - given-names: Pawel
    family-names: Renc
    orcid: 'https://orcid.org/0000-0002-0487-7454'
  - given-names: Nassim
    family-names: Oufattole
    orcid: 'https://orcid.org/0009-0002-9559-6216'
  - given-names: Robin
    family-names: van de Water
    orcid: 'https://orcid.org/0000-0002-2895-4872'
  - given-names: Patrick
    family-names: Rockenschaub
    orcid: 'https://orcid.org/0000-0002-6499-7933'
  - given-names: Ryan
    family-names: King
  - given-names: Simon
    family-names: Lee
    orcid: 'https://orcid.org/0000-0002-3324-9772'
  - given-names: Chao
    family-names: Pang
identifiers:
  - type: doi
    value: 10.5281/zenodo.17535783
repository-code: 'https://github.com/mmcdermott/MEDS_transforms'
abstract: >-
  MEDS-Transforms is a Python package for assembling complex
  data pre-processing workflows over MEDS datasets. To do
  this, you define a pipeline as a series of stages, each
  with its own arguments, then run the pipeline over your
  dataset. This allows the community to curate a library of
  shared stages for common operations, such as filtering,
  normalization, outlier detection, and more, which can be
  used to build novel pipelines for diverse use cases. Learn
  more below to see how MEDS-Transforms can help you build
  your data pipelines!
license: MIT