Package: MultivariateRandomForest 1.1.5
MultivariateRandomForest: Models Multivariate Cases Using Random Forests
Models and predicts multiple output features in single random forest considering the linear relation among the output features, see details in Rahman et al (2017)<doi:10.1093/bioinformatics/btw765>.
Authors:
MultivariateRandomForest_1.1.5.tar.gz
MultivariateRandomForest_1.1.5.zip(r-4.5)MultivariateRandomForest_1.1.5.zip(r-4.4)MultivariateRandomForest_1.1.5.zip(r-4.3)
MultivariateRandomForest_1.1.5.tgz(r-4.5-x86_64)MultivariateRandomForest_1.1.5.tgz(r-4.5-arm64)MultivariateRandomForest_1.1.5.tgz(r-4.4-x86_64)MultivariateRandomForest_1.1.5.tgz(r-4.4-arm64)MultivariateRandomForest_1.1.5.tgz(r-4.3-x86_64)MultivariateRandomForest_1.1.5.tgz(r-4.3-arm64)
MultivariateRandomForest_1.1.5.tar.gz(r-4.5-noble)MultivariateRandomForest_1.1.5.tar.gz(r-4.4-noble)
MultivariateRandomForest_1.1.5.tgz(r-4.4-emscripten)MultivariateRandomForest_1.1.5.tgz(r-4.3-emscripten)
MultivariateRandomForest.pdf |MultivariateRandomForest.html✨
MultivariateRandomForest/json (API)
| # Install 'MultivariateRandomForest' in R: |
| install.packages('MultivariateRandomForest', repos = c('https://razrahman.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 years agofrom:d0ba3dc651. Checks:12 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 23 2025 |
| R-4.5-win-x86_64 | OK | Mar 23 2025 |
| R-4.5-mac-x86_64 | OK | Mar 23 2025 |
| R-4.5-mac-aarch64 | OK | Mar 23 2025 |
| R-4.5-linux-x86_64 | OK | Mar 23 2025 |
| R-4.4-win-x86_64 | OK | Mar 23 2025 |
| R-4.4-mac-x86_64 | OK | Mar 23 2025 |
| R-4.4-mac-aarch64 | OK | Mar 23 2025 |
| R-4.4-linux-x86_64 | OK | Mar 23 2025 |
| R-4.3-win-x86_64 | OK | Mar 23 2025 |
| R-4.3-mac-x86_64 | OK | Mar 23 2025 |
| R-4.3-mac-aarch64 | OK | Mar 23 2025 |
Exports:build_forest_predictbuild_single_treeCrossValidationImputationNode_costpredictingsingle_tree_predictionsplit_nodesplitt2variable_importance_measure
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Prediction using Random Forest or Multivariate Random Forest | build_forest_predict |
| Model of a single tree of Random Forest or Multivariate Random Forest | build_single_tree |
| Generate training and testing samples for cross validation | CrossValidation |
| Imputation of a numerical vector | Imputation |
| Information Gain | Node_cost |
| Prediction of testing sample in a node | predicting |
| Prediction of Testing Samples for single tree | single_tree_prediction |
| Splitting Criteria of all the nodes of the tree | split_node |
| Split of the Parent node | splitt2 |
| Calculates variable Importance of a Regression Tree Model | variable_importance_measure |
