CRAN/E | mvMonitoring

mvMonitoring

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

Installation

About

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.

github.com/gabrielodom/mvMonitoring

Key Metrics

Version 0.2.4
R ≥ 2.10
Published 2023-11-21 164 days ago
Needs compilation? no
License GPL-2
CRAN checks mvMonitoring results

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Maintainer

Maintainer

Gabriel Odom

gabriel.odom@fiu.edu

Authors

Melissa Innerst

aut

Gabriel Odom

aut / cre

Ben Barnard

aut

Karen Kazor

aut

Amanda Hering

aut

Material

README
NEWS
Reference manual
Package source

Vignettes

Workflow

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

mvMonitoring archive

Depends

R ≥ 2.10

Imports

dplyr
lazyeval
plyr
rlang
utils
xts
zoo
robustbase
graphics

Suggests

testthat ≥ 3.0.0
knitr
rmarkdown