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mvMonitoring  

Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring
View on CRAN: Click here


Download and install mvMonitoring package within the R console
Install from CRAN:
install.packages("mvMonitoring")

Install from Github:
library("remotes")
install_github("cran/mvMonitoring")

Install by package version:
library("remotes")
install_version("mvMonitoring", "0.2.4")



Attach the package and use:
library("mvMonitoring")
Maintained by
Gabriel Odom
[Scholar Profile | Author Map]
First Published: 2017-10-20
Latest Update: 2023-11-21
Description:
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 , 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.
How to cite:
Gabriel Odom (2017). mvMonitoring: Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring. R package version 0.2.4, https://cran.r-project.org/web/packages/mvMonitoring. Accessed 07 May. 2025.
Previous versions and publish date:
0.1.0 (2017-10-20 10:55), 0.2.0 (2022-05-06 09:00), 0.2.2 (2023-06-30 20:30)
Other packages that cited mvMonitoring R package
View mvMonitoring citation profile
Other R packages that mvMonitoring depends, imports, suggests or enhances
Complete documentation for mvMonitoring
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