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
DOI:	10.32614/CRAN.package.sGMRFmix
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-oldrel (arm64): sGMRFmix_0.3.0.tgz, r-release (x86_64): sGMRFmix_0.3.0.tgz, r-oldrel (x86_64): sGMRFmix_0.3.0.tgz
Old sources:	sGMRFmix archive

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