Title: Oblique Trees for Classification Data
Description: Grows Oblique Trees to Classification Data.

Title: modified random Forest using non linear regression in the node
Depends: R (>= 2.0.0), stats, ROCR, pls, mda, e1071
Author: Bjoern Menze and Nico Splitthoff
Description: Classification (no regression up to now) based on a random
forest of trees using nonlinear regression in the nodes.
Maintainer: Bjoern Menze <menze@csail.mit.edu> and D.N. Splitthoff

Title: Oblique Random Forests from Recursive Linear Model Splits
Description: Random forest with oblique decision trees for binary
classification tasks. Discriminative node models in the tree
are based on: ridge regression, partial least squares
regression, logistic regression, linear support vector

Title: Oblique Random Forests for Right-Censored Time-to-Event Data
Authors@R: person("Byron", "Jaeger", email = "bcjaeger@uab.edu", role = c("aut", "cre"))
Description: Oblique random survival forests incorporate linear combinations of input variables into random survival forests (Ishwaran, 2008 <DOI:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <DOI:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.
Imports: Rcpp, pec, data.table, stats, missForest, purrr, glmnet,
survival, dplyr, rlang, prodlim, ggthemes, tidyr, ggplot2,

Authors@R: person("Byron", "Jaeger", email = "bjaeger@wakehealth.edu", role = c("aut", "cre"))

Title: An R package to assess agreement between observers.
Author: Teresa Henriques, Luis Antunes and Cristina Costa-Santos
Description: The package includes two functions for measuring agreement. Raw Agreement Indices (RAI) to categorical data and Information-Based Measure of Disagreement (IBMD) to continuous data. It can be used for multiple raters and multiple readings cases.
Packaged: 2013-12-04 15:13:40 UTC; teresahenriques

Title: Sensitivity analysis for Observational studies.
Description: Observational studies are limited in that there could be an unmeasured variable related to both the response variable and the primary predictor.  If this unmeasured variable were included in the analysis it would change the relationship (possibly changing the conclusions).  Sensitivity analysis is a way to see how much of a relationship needs to exist with the unmeasured variable before the conclusions change.  This package provides tools for doing a sensitivity analysis for regression (linear, logistic, and cox) style models.
Packaged: Wed Feb 20 16:42:33 2008; Greg