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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "sGMRFmix" in publications use:'
type: software
license: MIT
title: 'sGMRFmix: Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection'
version: 0.3.0
doi: 10.32614/CRAN.package.sGMRFmix
abstract: An implementation of sparse Gaussian Markov random field mixtures presented
  by Ide et al. (2016) <https://doi.org/10.1109/ICDM.2016.0119>. It provides a novel
  anomaly detection method for multivariate noisy sensor data. It can automatically
  handle multiple operational modes. And it can also compute variable-wise anomaly
  scores.
authors:
- family-names: Makiyama
  given-names: Koji
  email: hoxo.smile@gmail.com
repository: https://hoxo-m.r-universe.dev
commit: 4bf36707cf3e0a96c752f85c1ef2d98a632d9224
date-released: '2018-04-16'
contact:
- family-names: Makiyama
  given-names: Koji
  email: hoxo.smile@gmail.com