Title: Breiman and Cutler's random forests for classification and regression
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Title: Breiman and Cutler's random forests for classification and
Description: Classification and regression based on a forest of trees

Packaged: Thu Mar  3 16:04:52 2005; Liawand

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Packaged: Wed Apr  6 11:15:43 2005; Liawand

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Packaged: Mon May  2 13:06:44 2005; Liawand

Title: Breiman and Cutler's Random Forests for Classification and

Description: Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.

Title: Distributed 'randomForest' for Big Data using 'ddR' API
Author: Vishrut Gupta, Arash Fard, Winston Li, Matthew Saltz
Imports: ddR, stats, randomForest (>= 4.6-10)
Description: Distributed training and prediction of random forest models based upon 'randomForest' package.

Imports: ddR, stats, randomForest (>= 4.6-10), Matrix
Description: Distributed training and prediction of random forest models based upon 'randomForest' package using 'ddR' (Distributed Data Structures) API in the 'ddR' package.

Packaged: 2017-03-10 21:12:44 UTC; indrajitroy

Title: Explaining and Visualizing Random Forests in Terms of Variable
person("Aleksandra", "Paluszynska", email = "ola.paluszynska@gmail.com", role = c("aut")),
person("Yue", "Jiang", email = "rivehill@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9798-5517"))
Description: A set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).
randomForest (>= 4.6.12), ranger(>= 0.9.0), reshape2 (>=
Suggests: knitr, MASS (>= 7.3.47), testthat