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 fiu.edu> |
| License: |
GPL-2 |
| URL: |
https://github.com/gabrielodom/mvMonitoring |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
mvMonitoring results |
Documentation:
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