CITATION.cff

cff-version: 1.2.0
message: >-
  If you use PyBME in published work, please cite the software and the
  foundational BME references described below.
title: PyBME
abstract: >-
  PyBME is a pure-Python implementation of Bayesian Maximum Entropy (BME)
  geostatistics, including soft-data integration, space-time BME, SPDE/GMRF
  methods, Laplace approximation, EP, QMC, LIS, and network-domain BME.
  Its baseline network implementation uses a graph-Laplacian covariance on
  network topology, with separate SWMM and physics-informed examples for
  flow-informed priors. The PyBME-specific extensions are still under review
  and should be considered experimental until peer review is complete.
type: software
authors:
  - family-names: Wiesner-Friedman
    given-names: Corinne
version: 0.5.0
license: MIT
keywords:
  - geostatistics
  - BME
  - kriging
  - soft-data
  - spatial statistics
  - network-domain geostatistics
  - network kriging
  - network BME
  - graph Laplacian
  - river networks
  - sewer networks
preferred-citation:
  type: software
  authors:
    - family-names: Wiesner-Friedman
      given-names: Corinne
  title: PyBME
  version: 0.5.0
notes: >-
  PyBME is a Python implementation inspired by the MATLAB BMElib framework.
  For methodological attribution, please also cite the foundational BME
  references listed in the repository README. For network-domain work,
  PyBME-specific extensions are still under review and should be considered
  experimental until peer review is complete. PyBME's baseline
  graph-Laplacian covariance implementation is distinct
  from earlier Money-Serre network BME formulations and from Jat-Serre
  gradual-flow covariance BME formulations, which should be cited
  separately when used. Relevant external references include Money, Carter,
  and Serre (2009, Environ. Sci. Technol., doi:10.1021/es803236j); Money,
  Carter, and Serre (2009, Water Res., doi:10.1016/j.watres.2009.01.034);
  Jat and Serre (2016, Environ. Pollut.,
  doi:10.1016/j.envpol.2016.09.020); and Jat and Serre (2018, Stoch.
  Environ. Res. Risk Assess., doi:10.1007/s00477-018-1512-6). For the
  stream-network modelling software ecosystem, see Ver Hoef, Peterson, and
  Theobald (2006, doi:10.1007/s10651-006-0022-8), Ver Hoef and Peterson
  (2010, doi:10.1198/jasa.2009.ap08248), Peterson and Ver Hoef (2014,
  STARS, doi:10.18637/jss.v056.i02), and Dumelle et al. (2024, SSN2,
  doi:10.21105/joss.06389).