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: 0.2.4
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>
License: GPL-2
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mvMonitoring results


Reference manual: mvMonitoring.pdf
Vignettes: Workflow


Package source: mvMonitoring_0.2.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mvMonitoring_0.2.4.tgz, r-oldrel (arm64): mvMonitoring_0.2.4.tgz, r-release (x86_64): mvMonitoring_0.2.4.tgz, r-oldrel (x86_64): mvMonitoring_0.2.4.tgz
Old sources: mvMonitoring archive


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