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IntegratedMRF  

Integrated Prediction using Uni-Variate and Multivariate Random Forests
View on CRAN: Click here


Download and install IntegratedMRF package within the R console
Install from CRAN:
install.packages("IntegratedMRF")

Install from Github:
library("remotes")
install_github("cran/IntegratedMRF")

Install by package version:
library("remotes")
install_version("IntegratedMRF", "1.1.9")



Attach the package and use:
library("IntegratedMRF")
Maintained by
Raziur Rahman
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-04-16
Latest Update:
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.
How to cite:
Raziur Rahman (2016). IntegratedMRF: Integrated Prediction using Uni-Variate and Multivariate Random Forests. R package version 1.1.9, https://cran.r-project.org/web/packages/IntegratedMRF. Accessed 06 Oct. 2026.
Previous versions and publish date:
1.0 (2016-04-16 04:32), 1.1.4 (2016-07-22 22:42), 1.1.5 (2016-08-10 21:28), 1.1.8 (2017-06-22 22:36), 1.1.9 (2018-07-05 22:30), (2026-07-09 08:07)
Other packages that cited IntegratedMRF R package
View IntegratedMRF citation profile
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Complete documentation for IntegratedMRF
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