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.

Based on data of real user-agent strings, we can set filtering conditions
    and randomly sample user-agent strings from the user-agent string pool.

Performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a dimension reduction technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional projection matrix which results in reduced matrix. The projection matrix can be generated using the projection function that is independent to the original data. Then finally apply the classification task on the projected data.

Connects to the site <http://quotesondesign.com/> 
            that uses the 'WordPress' built-in REST API 
            to provide a way for you to grab quotes.

Provides several non parametric randomness tests for numeric sequences.

Provides (1) pseudo random generators - general linear congruential generators, 
    multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister
    algorithm (<doi:10.1007/978-3-540-74496-2_36>) and WELL (<doi:10.1145/1132973.1132974>) 
    generators); (2) quasi random generators - the Torus algorithm, the
    Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some
    generator tests - the gap test, the serial test, the poker test, see, e.g., 
    Gentle (2003) <doi:10.1007/b97336>. 
    Take a look at the Distribution task view of types and tests of random number generators.
    The package can be provided without the 'rngWELL' dependency on demand.
    Package in Memoriam of Diethelm and Barbara Wuertz.