boot, fastGHQuad, Matrix, Rfast, parallel, doParallel, doRNG,
foreach

Random name generating function and data set
Function to generate random gender and ethnicity correct
first and/or last names. Names are chosen proportionally based
upon their probability of appearing in a large scale data base
of real names.

Function to generate random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.

Function for Generating Random Names and a Dataset
Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.

c(person(given=c("Damian", "W."), family="Betebenner", email="dbetebenner@nciea.org", role=c("aut", "cre")))

crayon, data.table (>= 1.12.0), toOrdinal (>= 1.1)

crayon, data.table (>= 1.14.0), toOrdinal (>= 1.1)

Ishwaran and Kogalur's Random Survival Forest
Hemant Ishwaran <hemant.ishwaran@gmail.com>, Udaya B. Kogalur <ubk2101@columbia.edu>
Ensemble survival analysis based on a random forest of trees using random inputs.
GNU General Public License Version 2 or higher.

Hemant Ishwaran <hemant.ishwaran@gmail.com>, Udaya B. Kogalur
<ubk2101@columbia.edu>
Ensemble survival analysis based on a random forest of
trees using random inputs.

Hemant Ishwaran <hemant.ishwaran@gmail.com> and Udaya B.
Kogalur <kogalurshear@gmail.com>
Random survival forests for right-censored and competing
risks survival data.

Random Uniform Forests for Classification and Regression
Ensemble model, for classification and regression, based on a forest of of unpruned and randomized binary 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 Uniform law on the support of each candidate variable. Optimal random node is, then, selected by maximizing information gain (classification) or minimizing 'L2' (or 'L1') distance (regression). Data are either bootstrapped or subsampled for each tree. Random Uniform Forests are aimed to lower correlation between trees, to offer more details about variable importance and selection and to allow native incremental learning.
methods, Rcpp (>= 0.11.1), parallel, doParallel, foreach (>=
1.4.2), ggplot2, pROC, gtools