mvMonitoring: Multi-State Adaptive Dynamic Principal Component Analysis forMultivariate 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:
Downloads:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=mvMonitoringto link to this page.