Package: mvMonitoring Type: Package Title: Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring Version: 0.2.4 Date: 2023-11-21 Authors@R: c( person( "Melissa", "Innerst", email = "innerst@juniata.edu", role = c("aut") ), person( "Gabriel", "Odom", email = "gabriel.odom@fiu.edu", role = c("aut", "cre") ), person( "Ben", "Barnard", email = "ben_barnard@baylor.edu", role = c("aut") ), person("Karen", "Kazor", role = c("aut")), person( "Amanda", "Hering", email = "mandy_hering@baylor.edu", role = c("aut") ) ) 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. License: GPL-2 Depends: R (>= 2.10) Imports: dplyr, lazyeval, plyr, rlang, utils, xts, zoo, robustbase, graphics Encoding: UTF-8 LazyData: true RoxygenNote: 7.2.3 Suggests: testthat (>= 3.0.0), knitr, rmarkdown VignetteBuilder: knitr URL: https://github.com/gabrielodom/mvMonitoring Config/testthat/edition: 3 Repository: https://gabrielodom.r-universe.dev Date/Publication: 2023-11-21 17:10:29 UTC RemoteUrl: https://github.com/gabrielodom/mvmonitoring RemoteRef: HEAD RemoteSha: 1b081c8866a963f69c530487901e13aca3623034 NeedsCompilation: no Packaged: 2026-10-10 15:21:25 UTC; root Author: Melissa Innerst [aut], Gabriel Odom [aut, cre], Ben Barnard [aut], Karen Kazor [aut], Amanda Hering [aut] Maintainer: Gabriel Odom