Package: IntegratedMRF
Type: Package
Title: Integrated Prediction using Uni-Variate and Multivariate Random
        Forests
Version: 1.1.9
Date: 2018-07-05
Author: Raziur Rahman, Ranadip Pal 
Maintainer: Raziur Rahman <razeeebuet@gmail.com>
Description: An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach. 
License: GPL-3
RoxygenNote: 6.0.1
Depends: R (>= 2.10)
Imports: Rcpp (>= 0.12.4), bootstrap, ggplot2, utils, stats, limSolve,
        MultivariateRandomForest
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2018-07-05 20:10:58 UTC; Raziur_Rahman
Repository: CRAN
Date/Publication: 2018-07-05 20:30:03 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 12:36:10 UTC; windows
Archs: i386, x64
