Simulate a Gaussian vector with 'p' independent components of length 'n'. Parameters of each component are uniformly random and are taken between -10 and 10, with (absolute) standard deviation equals mean.

simulationData

Ensemble model, for classification, regression
	and unsupervised learning, based on a forest of unpruned 
	and randomized binary decision trees. Each tree is grown 
	by sampling, with replacement, a set of variables at each node. 
	Each cut-point is generated randomly, according to the continuous 
	Uniform distribution. For each tree, data are either bootstrapped 
	or subsampled. The unsupervised mode introduces clustering, dimension reduction
	and variable importance, using a three-layer engine. Random Uniform Forests are mainly 
	aimed to lower correlation between trees (or trees residuals), to provide a deep analysis 
	of variable importance and to allow native distributed and incremental learning.

simulationData: Simulation of Gaussian vector

Description

Usage

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Examples