CITATION.cff

cff-version: 1.2.0
message: "If you use this software, please cite it using the metadata below."
type: software
title: "BlockInvGPFA"
abstract: >-
  A scikit-learn-compatible implementation of Gaussian-process factor
  analysis using block matrix inversion for efficient inference with
  variable-length time series.
authors:
  - family-names: "Musangu"
    given-names: "Brooks M."
    email: "brooks_musangu@hms.harvard.edu"
    affiliation: "Department of Neurobiology, Harvard Medical School"
    orcid: "https://orcid.org/0000-0002-7137-3707"
  - family-names: "Drugowitsch"
    given-names: "Jan"
    email: "jan_drugowitsch@hms.harvard.edu"
    affiliation: "Department of Neurobiology, Harvard Medical School"
    orcid: "https://orcid.org/0000-0002-7846-0408"
version: "0.1.0"
license: "BSD-3-Clause"
repository-code: "https://github.com/CausalityInMotion/BlockInverseGPFA"
url: "https://blockinversegpfa.readthedocs.io/en/latest/"
keywords:
  - "Gaussian Processes"
  - "block matrices"
  - "latent variable models"