Title: Random Uniform Forests for Classification, Regression and
Description: Ensemble model, for classification, regression and unsupervised learning, base on a forest of unpruned and randomized binary decision trees.
foreach (>= 1.4.2), ggplot2, pROC, gtools, cluster, MASS

Description: Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees.

Description: 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.

foreach (>= 1.4.2), ggplot2, pROC, cluster, MASS

Title: Generate Attractive Random Colors
Description: Simple methods to generate attractive random colors. The random
colors are from a wrapper of 'randomColor.js'
<https://github.com/davidmerfield/randomColor>. In addition, it also generates
optimally distinct colors based on k-means (inspired by 'IWantHue'
Imports: colorspace, stringr, V8, stats, methods

Imports: colorspace, stringr, V8, stats, methods, scales, Rtsne,

Author: Joseph J. Lee and Tirthankar Dasgupta
Maintainer: Joseph J. Lee <lee26@fas.harvard.edu>
Description: The 'randomizationInference' package allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and intervals for any estimand of interest, according to the specified null and alternative hypotheses.  The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.
Packaged: 2013-09-10 03:51:48 UTC; Joseph J. Lee