IntegratedMRF: Integrated Prediction using Uni-Variate and Multivariate Random Forests
Version 1.1.8

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.

Getting started

Package details

AuthorRaziur Rahman, Ranadip Pal
Date of publication2017-06-22 20:36:01 UTC
MaintainerRaziur Rahman <>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the IntegratedMRF package in your browser

Any scripts or data that you put into this service are public.

IntegratedMRF documentation built on June 23, 2017, 4:45 a.m.