Description: Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed  without making any parametric assumptions on the stable (in-control) distribution.

Package: dfphase1
Type: Package
Title: Phase I Control Charts (with Emphasis on Distribution-Free
Methods)
Version: 1.1.3
Author: Giovanna Capizzi and Guido Masarotto
Maintainer: Giovanna Capizzi <giovanna.capizzi@unipd.it>
Description: Statistical methods for retrospectively detecting changes
in location and/or dispersion of univariate and multivariate
variables. Data values are assumed to be independent, can be
individual (one observation at each instant of time) or subgrouped
(more than one observation at each instant of time).
Control limits are computed, often using a permutation approach,
so that a prescribed false alarm probability is guaranteed
without making any parametric assumptions on the stable
(in-control) distribution. See G. Capizzi and G. Masarotto (2018)
<doi:10.1007/978-3-319-75295-2> for an introduction to the package.
License: LGPL (>= 2)
Imports: Rcpp (>= 0.11.1), lattice, robustbase
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2021-02-17 12:59:59 UTC; guido
Repository: CRAN
Date/Publication: 2021-02-17 15:50:13 UTC

Package: dfphase1
Type: Package
Title: Phase I Control Charts (with Emphasis on Distribution-Free
Methods)
Version: 1.1.4
Author: Giovanna Capizzi and Guido Masarotto
Maintainer: Giovanna Capizzi <giovanna.capizzi@unipd.it>
Description: Statistical methods for retrospectively detecting changes
in location and/or dispersion of univariate and multivariate
variables. Data values are assumed to be independent, can be
individual (one observation at each instant of time) or subgrouped
(more than one observation at each instant of time).
Control limits are computed, often using a permutation approach,
so that a prescribed false alarm probability is guaranteed
without making any parametric assumptions on the stable
(in-control) distribution. See G. Capizzi and G. Masarotto (2018)
<doi:10.1007/978-3-319-75295-2_1> for an introduction to the package.
License: LGPL (>= 2)
Imports: Rcpp (>= 0.11.1), lattice, robustbase
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2021-12-01 11:25:11 UTC; guido
Repository: CRAN
Date/Publication: 2021-12-01 12:20:02 UTC