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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.4 |
rolling linux/jammy R-4.5 | mvMonitoring_0.2.4.tar.gz |
2.7 MiB |
0.2.4 |
rolling linux/noble R-4.5 | mvMonitoring_0.2.4.tar.gz |
2.7 MiB |
0.2.4 |
rolling source/ R- | mvMonitoring_0.2.4.tar.gz |
3.0 MiB |
0.2.4 |
latest linux/jammy R-4.5 | mvMonitoring_0.2.4.tar.gz |
2.7 MiB |
0.2.4 |
latest linux/noble R-4.5 | mvMonitoring_0.2.4.tar.gz |
2.7 MiB |
0.2.4 |
latest source/ R- | mvMonitoring_0.2.4.tar.gz |
3.0 MiB |
0.2.4 |
2026-04-26 source/ R- | mvMonitoring_0.2.4.tar.gz |
3.0 MiB |
0.2.4 |
2026-04-23 source/ R- | mvMonitoring_0.2.4.tar.gz |
3.0 MiB |
0.2.4 |
2026-04-09 windows/windows R-4.5 | mvMonitoring_0.2.4.zip |
2.7 MiB |
0.2.4 |
2025-04-20 source/ R- | mvMonitoring_0.2.4.tar.gz |
3.0 MiB |