R: Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring

# Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring

## Documentation for package ‘mvMonitoring’ version 0.2.4

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

mvMonitoring-package | A Package for Multivariate Statistical Process Monitoring
dataStateSwitch | Alternate Observations in a Data Frame over States
fault1A_xts | Process Data under a System Shift Fault
fault2A_xts | Process Data under a System Drift Fault
fault3A_xts | Process Data under a System Signal Amplification
faultDetect | Process Fault Detection
faultFilter | Process Fault Filtering
faultSwitch | Induce the Specified Fault on NOC Observations
mspContributionPlot | Contribution Plots
mspMonitor | Real-Time Process Monitoring Function
mspProcessData | Simulate Normal or Fault Observations from a Single-State or Multi-State Process
mspSPEPlot | Squared Prediction Error Contribution Plots
mspSubset | Multi-State Subsetting
mspT2Plot | T-Squared Contribution Plots
mspTrain | Multi-State Adaptive-Dynamic Process Training
mspWarning | Process Alarms
mvMonitoring | A Package for Multivariate Statistical Process Monitoring
normal_switch_xts | Process Data under Normal Conditions
oneDay_clean | Real Process Data for Testing
pca | PCA for Data Scatter Matrix
processMonitor | Adaptive Process Training
processNOCdata | Simulate NOC Observations from a Single-State or Multi-State Process
quantile.density | Extract Quantiles from 'density' Objects
rotate3D | Three-Dimensional Rotation Matrix
rotateScale3D | Three-Dimensional Rotation and Scaling Matrix
tenDay_clean | Real Process Data for Training
threshold | Non-parametric Threshold Estimation