(patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019
Suggests: knitr, rmarkdown, MASS, BART, survRM2, TH.data, coin, rpart,

Suggests: knitr, rmarkdown, MASS, BART, pROC, survRM2, TH.data,
sandwich, coin, rpart, testthat (>= 2.1.0)

models (counterfactual patient-level quantities, such as the conditional average treatment effect),
and (4) treatment effect estimation and inference (for the overall population and discovered
subgroups). These tools can be customized and are directly used in PRISM
<arXiv:1912.03337>. This package is in beta and will be continually updated.

Title: Builds Trees by Sampling Variables from Groups
Description: Random Forest that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it contains variables from each group. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees.
Trees are grown using the C5.0 algorithm. Currently works for classification only.
Packaged: 2017-06-21 07:43:04 UTC; david.cortes

Title: Builds Trees by Sampling Variables in Groups
Description: Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees.
Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options.
As it's built with C5.0 trees, it works only for classification (not for regression).
Packaged: 2017-06-30 11:40:13 UTC; david.cortes