The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.
| Version: | 1.1.9 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp (≥ 0.12.4), bootstrap, ggplot2, utils, stats, limSolve, MultivariateRandomForest |
| LinkingTo: | Rcpp |
| Published: | 2018-07-05 |
| DOI: | 10.32614/CRAN.package.IntegratedMRF |
| Author: | Raziur Rahman, Ranadip Pal |
| Maintainer: | Raziur Rahman <razeeebuet at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| CRAN checks: | IntegratedMRF results |
| Reference manual: | IntegratedMRF.pdf |
| Package source: | IntegratedMRF_1.1.9.tar.gz |
| Windows binaries: | r-devel: IntegratedMRF_1.1.9.zip, r-release: IntegratedMRF_1.1.9.zip, r-oldrel: IntegratedMRF_1.1.9.zip |
| macOS binaries: | r-release (arm64): IntegratedMRF_1.1.9.tgz, r-oldrel (arm64): IntegratedMRF_1.1.9.tgz, r-release (x86_64): IntegratedMRF_1.1.9.tgz, r-oldrel (x86_64): IntegratedMRF_1.1.9.tgz |
| Old sources: | IntegratedMRF archive |
Please use the canonical form https://CRAN.R-project.org/package=IntegratedMRF to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.