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.
- Version0.2.4
- R version≥ 2.10
- LicenseGPL-2
- Needs compilation?No
- Last release11/21/2023
Documentation
Team
Gabriel Odom
Ben Barnard
Show author detailsRolesAuthorMelissa Innerst
Show author detailsRolesAuthorKaren Kazor
Show author detailsRolesAuthorAmanda Hering
Show author detailsRolesAuthor
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports7 packages
- Suggests3 packages