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: 0.2.4 Depends: R (≥ 2.10) Imports: dplyr, lazyeval, plyr, rlang, utils, xts, zoo, robustbase, graphics Suggests: testthat (≥ 3.0.0), knitr, rmarkdown Published: 2023-11-21 DOI: 10.32614/CRAN.package.mvMonitoring Author: Melissa Innerst [aut], Gabriel Odom [aut, cre], Ben Barnard [aut], Karen Kazor [aut], Amanda Hering [aut] Maintainer: Gabriel Odom <gabriel.odom at fiu.edu> License: GPL-2 URL: https://github.com/gabrielodom/mvMonitoring NeedsCompilation: no Materials: README NEWS CRAN checks: mvMonitoring results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=mvMonitoring to link to this page.
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