Package: SNSchart
Type: Package
Title: Sequential Normal Scores in Statistical Process Management
Version: 1.4.0
Authors@R: c(person("Victor", "Tercero", email = "victor.tercero@tec.mx", role=c("aut")),
  person("Luis", "Benavides", email = "luisbv@tec.mx", role = c("aut", "cre")),
  person("Jorge", "Merlo", email = "jorgemerlo24@gmail.com", role = c("ctb")))
Author: Victor Tercero [aut],
  Luis Benavides [aut, cre],
  Jorge Merlo [ctb]
Maintainer: Luis Benavides <luisbv@tec.mx>
Description: The methods discussed in this package are new non-parametric methods
    based on sequential normal scores 'SNS' (Conover et al (2017)
    <doi:10.1080/07474946.2017.1360091>), designed for sequences of observations,
    usually time series data, which may occur singly or in batches,
    and may be univariate or multivariate. These methods are designed
    to detect changes in the process, which may occur as changes in location
    (mean or median), changes in scale (standard deviation, or variance), or
    other changes of interest in the distribution of the observations,
    over the time observed. They usually apply to large data sets,
    so computations need to be simple enough to be done in a reasonable
    time on a computer, and easily updated as each new observation
    (or batch of observations) becomes available. Some examples and more detail
    in 'SNS' is presented in the work by Conover et al (2019) <arXiv:1901.04443>.
Depends: R (>= 2.10)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: parallel, stats, MASS
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-04-05 18:22:03 UTC; L03099908
Repository: CRAN
Date/Publication: 2021-04-07 08:40:15 UTC
Built: R 4.6.0; ; 2026-04-27 03:35:29 UTC; unix
