A.R. (Rob) Kronlund <allen.kronlund@dfo-mpo.gc.ca>, and
The R directory '.../library/PBSmodelling/doc' offers a complete user

Title: GUI Tools Made Easy: Interact with Models and Explore Data
Description: Provides software to facilitate the design, testing, and operation
of computer models. It focuses particularly on tools that make it easy to
construct and edit a customized graphical user interface (GUI). Although our
simplified GUI language depends heavily on the R interface to the Tcl/Tk
package, a user does not need to know Tcl/Tk. Examples illustrate models
built with other R packages, including PBSmapping, PBSddesolve, and BRugs.
A complete user's guide `PBSmodelling-UG.pdf' shows how to use this package

Author: Jon T. Schnute [aut], Alex Couture-Beil [aut],
Rowan Haigh [aut, cre], Nicholas Boers [ctb], Anisa Egeli [ctb],
A. R. Kronlund [ctb], Steve Martell [ctb], Norm Olsen [ctb]
Copyright: 2005-2017, Fisheries and Oceans Canada
construct and edit a customized graphical user interface ('GUI'). Although our
simplified 'GUI' language depends heavily on the R interface to the 'Tcl/Tk'
package, a user does not need to know 'Tcl/Tk'. Examples illustrate models
built with other R packages, including 'PBSmapping', 'PBSddesolve', and 'BRugs'.
A complete user's guide 'PBSmodelling-UG.pdf' shows how to use this package

Copyright: 2005-2019, Fisheries and Oceans Canada

Title: Segmenting Multiple Time Series by Contemporaneous Linear
Author: Jinyuan Chang, Bin Guo and Qiwei Yao
Description: To seek for a contemporaneous linear transformation for
a multivariate time series such that the transformed series is segmented
into several lower-dimensional subseries, and those subseries are
uncorrelated with each other both contemporaneously and serially.

Title: PCA Plotting in a User Friendly Way
person("Martin", "Berntsson", , "aftonsangen@gmail.com", c("aut", "cre")),
person("Philip Anton", "de Saint-Aubain", , "philip@saint-aubain.dk", "aut")
Description: A central part of using PCA and similar methods are creating and using plots. Visual inspection of these plots is an extremely powerful way of understanding your matrix-form data. Doing this is essential when applying multivariate methods on real-life matrix data. This package provides plots, tailor made for understanding a PCA model on a matrix dataset. We have worked to design plots that supports the mathematical properties of PCA, but still easy to use and understand. The flexibility of the plotting capacity of R has been utilized to create a look and feel that supports the understanding of the PCA model. In addition to that, this package contains a prediction function, using PCA to fill "holes" in a matrix. This package does not provide you with the scores and loadings etc in numeric form. This is provided through the underlying package 'mixOmics' which is the basis of this package. In addition, applied PCA on real life data requires flexibility. The original way of calculating a PCA is by using the method SVD, used in e.g. 'stats' package. This is preferred from a precision point of view. But 'PCA4you' is based on the method NIPALS for calculating the PCA model. Hence, the following cases, which are not (or poorly) supported by SVD, are fully supported by 'PCA4you':  1) Non complete matrices with little or a lot of missing values 2) More row than columns, sometimes a huge difference 3) More columns than rows, sometimes a huge difference 4) Complete decomposition is not possible or desired. Sometimes all components can not be calculated for different reasons. Big data, system resources or time can limit the desired number of principal components.