quadprog (>= 1.5-3), kernlab, LowRankQP, limma, OCplus

Imports: Matrix (>= 1.0-0), numDeriv, limSolve (>= 1.5.2), rgl,
scatterplot3d, qvalue, Iso(>= 0.0-5), quadprog (>= 1.5-3),
Enhances: stats, graphics, limma, OCplus

Title: Estimating the Proportion of True Null Hypotheses for FDR
person(list("Yudi","Alexander"), list("Pawitan", "Ploner"), role="aut", comment="The author(s) of OCplus::tMixture, which was modified slightly leading to discTMix in this package."),
person(list('Egil', 'Mette', 'Marcus'), list('Ferkingstad', 'Langaas', 'Davy') , role="aut", comment="The author(s) of limma::convest, which was modified slightly leading to convest in this package.")
kernlab, LowRankQP, graphics, grDevices, methods, stats
Description: Methods for estimating the proportion of true null hypotheses, i.e., the pi0, when a very large number of hypotheses are simultaneously tested, especially for the purpose of (local) false discovery rate control for microarray data. It also contains functions to estimate the distribution of noncentrality parameters from a large number of parametric tests.
Yudi Pawitan [aut] (The author(s) of OCplus::tMixture, which was
modified slightly leading to discTMix in this package.),
Alexander Ploner [aut] (The author(s) of OCplus::tMixture, which was
Egil Ferkingstad [aut] (The author(s) of limma::convest, which was
modified slightly leading to convest in this package.),
Mette Langaas [aut] (The author(s) of limma::convest, which was
Marcus Davy [aut] (The author(s) of limma::convest, which was modified
slightly leading to convest in this package.)

Imports: Matrix (>= 1.0-0), numDeriv, limSolve (>= 1.5.2),

Title: Prediction Intervals for Random Forests
Description: Implements multiple state-of-the-art prediction interval methodologies for random forests.
These include: quantile regression intervals, out-of-bag intervals, bag-of-observations intervals,
one-step boosted random forest intervals, bias-corrected intervals, high-density intervals, and
split-conformal intervals. The implementations include a combination of novel adjustments to the
original random forest methodology and novel prediction interval methodologies. All of these
methodologies can be utilized using solely this package, rather than a collection of separate
packages. Currently, only regression trees are supported. Also capable of handling high dimensional data.
Ghosal, Indrayudh and Hooker, Giles (2018) <arXiv:1803.08000>.
Zhu, Lin and Lu, Jiaxin and Chen, Yihong (2019) <arXiv:1905.10101>.
Zhang, Haozhe and Zimmerman, Joshua and Nettleton, Dan and Nordman, Daniel J. (2019) <doi:10.1080/00031305.2019.1585288>.
Romano, Yaniv and Patterson, Evan and Candes, Emmanuel (2019) <arXiv:1905.03222>.
Tung, Nguyen Thanh and Huang, Joshua Zhexue and Nguyen, Thuy Thi and Khan, Imran (2014) <doi:10.13140/2.1.2500.8002>.
Suggests: testthat, devtools, foreach, doParallel, hdrcde, rfinterval,
Packaged: 2020-05-09 00:05:56 UTC; thechanceyman
Author: Chancellor Johnstone [cre, aut, cph],