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.

Package details

AuthorMelissa Johnson [aut], Gabriel Odom [aut, cre], Ben Barnard [aut], Karen Kazor [aut], Amanda Hering [aut]
MaintainerGabriel Odom <[email protected]>
LicenseGPL-2
Version0.1.0
URL https://github.com/gabrielodom/mvMonitoring
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("mvMonitoring")

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mvMonitoring documentation built on Nov. 17, 2017, 6:31 a.m.