regression machines suggested by the context and provided by
the user. If the user has no valuable knowledge on the data,
five defaults machines wrappers are implemented so as to cover
a minimal spectrum of prediction methods. If necessary, the
computations may be parallelized. The method is described in
Biau, Fischer, Guedj and Malley (2013), "COBRA: A Nonlinear

Description: This package performs prediction for regression-oriented problems, aggregating in a nonlinear scheme any basic regression machines suggested by the context and provided by the user. If the user has no valuable knowledge on the data, four defaults machines wrappers are implemented so as to cover a minimal spectrum of prediction methods. If necessary, the computations may be parallelized. The method is described in Biau, Fischer, Guedj and Malley (2013), "COBRA: A Nonlinear Aggregation Strategy".
Suggests: snowfall, lars, ridge, tree, randomForest

problems, aggregating in a nonlinear scheme basic regression
machines such as the Lasso, random forests, or any other

Title: COmbat CO-Normalization Using conTrols (COCONUT)
Author: Timothy E Sweeney, MD, PhD [aut,cre]
Maintainer: Timothy E Sweeney <tes17@alumni.stanford.edu>
Description: Allows for pooled analysis of microarray data by batch-correcting control samples, and then applying the derived correction parameters to non-control samples to obtain bias-free, inter-dataset corrected data.

Packaged: 2017-09-19 02:35:09 UTC; timsweeney

Title: Composite Indicator Construction and Analysis
Description: A comprehensive high-level package for composite indicator construction and analysis. It is a "development environment"
for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation,
data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for
principle component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single
hierarchical list called a "COIN". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons.
It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results (maps,
Finally, three Shiny apps are available which enable fast data exploration, results presentation, and checking the effects of
Full documentation is found in the online book at <https://bluefoxr.github.io/COINrDoc/>, as well as the vignette.
Authors@R: person("William", "Becker", email = "william.becker@bluefoxdata.eu",