Depends: R (>= 2.2.0), tensorA, robustbase, bayesm

Imports: methods, utils, grDevices, stats, tensorA, robustbase, bayesm,
Suggests: rgl, combinat, energy, knitr, rmarkdown

Package: compositionsGUI
Type: Package
Title: Graphical User Environment for Compositional Data Analysis
Version: 1.0
Date: 2013-05-14
Author: Jiri Eichler, Karel Hron, Raimon Tolosana-Delgado, Gerald van
den Boogaart, Matthias Templ, Peter Filzmoser
Maintainer: Jiri Eichler <eichler@subreg.cz>
Depends: robCompositions, compositions, mvoutlier, robustbase, RGtk2,
MASS, nnet, boot
Description: The package provides graphical user interface and teaching
tool for compositional data analysis using R. It contains data
loading from various sources (external sources, installed R
packages), preprocessing (imputation of missing values and
rounded zeros), basic summary statistics as well as log-ratio
transformations, necessary for a reasonable statistical
treatment of compositional data (isometric, centred, additive
log-ratio transformations). Further, also statistical methods
like outlier detection and principal component analysis,
together with the corresponding graphical tools, are provided.
Finally, the package contains plotting functions, suitable for
compositional data analysis, like ternary and quaternary
diagrams, scatterplots of log-ratio transformed data, and
diagrams with categorical/continuous covariates.
License: GPL (>= 2)
Packaged: 2013-05-16 20:20:57 UTC; templ
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-05-24 12:34:41

Title: Compositional, Multivariate and Univariate Time Series Outlier
person("Sevvandi", "Kandanaarachchi", email = "sevvandik@gmail.com",
person("Patricia", "Menendez", email = "Patricia.Menendez@monash.edu",
person("Ursula", "Laa", email = "Ursula.Laa@monash.edu",
person("Ruben", "Loaiza-Maya", email = "Ruben.LoaizaMaya@monash.edu",
Description: An ensemble of time series outlier detection methods that can be used for
compositional, multivariate and univariate data. It uses the four R packages
'forecast', 'tsoutliers', 'otsad' and 'anomalize' to detect time series outliers.
Imports: otsad, tsoutliers, forecast, anomalize, dplyr, tibble, rlang,
pracma, dobin, ICS, fastICA, gridExtra, grid, ggplot2, tidyr,
Suggests: knitr, rmarkdown, tourr, stringr, broom, rgdal