doi: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.">

sGMRFmix: Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection (original) (raw)

An implementation of sparse Gaussian Markov random field mixtures presented by Ide et al. (2016) <doi: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.

Version: 0.3.0
Imports: ggplot2, glasso, mvtnorm, stats, tidyr, utils, zoo
Suggests: dplyr, ModelMetrics, testthat, covr, knitr, rmarkdown
Published: 2018-04-16
DOI: 10.32614/CRAN.package.sGMRFmix
Author: Koji Makiyama [cre, aut]
Maintainer: Koji Makiyama <hoxo.smile at gmail.com>
License: MIT + file
NeedsCompilation: no
Materials: NEWS
CRAN checks: sGMRFmix results

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