This package performs prediction for regression-oriented
problems, aggregating in a nonlinear scheme any basic
regression machines suggested by the context and possibly
provided by the user. If necessary, the computations may be
parallelized. The method is described in Biau, Fischer, Guedj
and Malley (2013), "COBRA: A Nonlinear Aggregation Strategy".
snowfall, lars, ridge, FNN, tree, randomForest

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,
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
Aggregation Strategy".

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".
snowfall, lars, ridge, tree, randomForest

This package performs prediction for regression-oriented
problems, aggregating in a nonlinear scheme basic regression
machines such as the Lasso, random forests, or any other
provided by the user. If necessary, the computations may be
parallelized. The method is described in Biau, Fischer, Guedj
and Malley (2013), "COBRA: A Nonlinear Aggregation Strategy".

COmbat CO-Normalization Using conTrols (COCONUT)
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.