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 , 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.


Reference manual

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0.1.0 by Gabriel Odom, a year ago

Browse source code at

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

Documentation:   PDF Manual  

GPL-2 license

Imports BMS, dplyr, lazyeval, plyr, rlang, utils, xts, zoo, robustbase, graphics

Suggests knitr, rmarkdown

See at CRAN