Package: mvMonitoring 0.2.4

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.

Authors:Melissa Innerst [aut], Gabriel Odom [aut, cre], Ben Barnard [aut], Karen Kazor [aut], Amanda Hering [aut]

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Bug tracker:https://github.com/gabrielodom/mvmonitoring/issues

On CRAN:

18 exports 4 stars 1.33 score 24 dependencies 29 scripts 243 downloads

Last updated 10 months agofrom:1b081c8866. Checks:OK: 7. Indexed: yes.

Exports:dataStateSwitchfaultDetectfaultFilterfaultSwitchmspContributionPlotmspMonitormspProcessDatamspSPEPlotmspSubsetmspT2PlotmspTrainmspWarningpcaprocessMonitorprocessNOCdatarotate3DrotateScale3Dthreshold

Dependencies:cliDEoptimRdplyrfansigenericsgluelatticelazyevallifecyclemagrittrpillarpkgconfigplyrR6Rcpprlangrobustbasetibbletidyselectutf8vctrswithrxtszoo