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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.
Getting started
Browse package contents
Package details |
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| Author | Melissa Innerst [aut], Gabriel Odom [aut, cre], Ben Barnard [aut], Karen Kazor [aut], Amanda Hering [aut] |
| Maintainer | Gabriel Odom <gabriel.odom@fiu.edu> |
| License | GPL-2 |
| Version | 0.2.4 |
| URL | https://github.com/gabrielodom/mvMonitoring |
| Package repository | View on CRAN |
| Installation |
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