sGMRFmix: Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection

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
Author: Koji Makiyama [cre, aut]
Maintainer: Koji Makiyama <hoxo.smile at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: NEWS
CRAN checks: sGMRFmix results

Documentation:

Reference manual: sGMRFmix.pdf
Vignettes: Sparse Gaussian MRF Mixtures for Anomaly Detection

Downloads:

Package source: sGMRFmix_0.3.0.tar.gz
Windows binaries: r-devel: sGMRFmix_0.3.0.zip, r-release: sGMRFmix_0.3.0.zip, r-oldrel: sGMRFmix_0.3.0.zip
macOS binaries: r-release (arm64): sGMRFmix_0.3.0.tgz, r-release (x86_64): sGMRFmix_0.3.0.tgz, r-oldrel: sGMRFmix_0.3.0.tgz
Old sources: sGMRFmix archive

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