DESCRIPTION

Package: mvMonitoring
Type: Package
Title: Multi-State Adaptive Dynamic Principal Component Analysis for
    Multivariate Process Monitoring
Version: 0.1.0
Authors@R: c(person("Melissa", "Johnson", email = "melissa_johnson2@baylor.edu", role = c("aut")),
  person("Gabriel", "Odom", email = "gabriel.odom@med.miami.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")))
Maintainer: Gabriel Odom <gabriel.odom@med.miami.edu>
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 <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.
License: GPL-2
Depends:
    R (>= 2.10)
Imports:
    dplyr,
    lazyeval,
    plyr,
    rlang,
    utils,
    xts,
    zoo,
    robustbase,
    graphics
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Suggests:
    testthat (>= 3.0.0),
    knitr,
    rmarkdown
VignetteBuilder: knitr
URL: https://github.com/gabrielodom/mvMonitoring
Config/testthat/edition: 3