mvMonitoring: Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring

Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to data generated from a continuous-time multivariate industrial or natural process. Employ PCA-based dimension reduction to extract linear combinations of relevant features, reducing computational burdens. For a description of ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al. The multi-state application of ADPCA is from a manuscript under current revision entitled "Multi-State Multivariate Statistical Process Control" by Odom, Newhart, Cath, and Hering, and is expected to appear in Q1 of 2018.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: BMS, dplyr, lazyeval, plyr, rlang, utils, xts, zoo, robustbase, graphics
Suggests: knitr, rmarkdown
Published: 2017-10-20
Author: Melissa Johnson [aut], Gabriel Odom [aut, cre], Ben Barnard [aut], Karen Kazor [aut], Amanda Hering [aut]
Maintainer: Gabriel Odom <gabriel.odom at med.miami.edu>
License: GPL-2
URL: https://github.com/gabrielodom/mvMonitoring
NeedsCompilation: no
CRAN checks: mvMonitoring results

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Reference manual: mvMonitoring.pdf
Vignettes: Workflow
Package source: mvMonitoring_0.1.0.tar.gz
Windows binaries: r-prerel: mvMonitoring_0.1.0.zip, r-release: mvMonitoring_0.1.0.zip, r-oldrel: mvMonitoring_0.1.0.zip
OS X binaries: r-prerel: mvMonitoring_0.1.0.tgz, r-release: mvMonitoring_0.1.0.tgz

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