Showing 4 of total 4 results (show query)
r-forge
partykit:A Toolkit for Recursive Partytioning
A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.
Maintained by Torsten Hothorn. Last updated 22 days ago.
12.71 score 2.3k scripts 97 dependents
bioc
cola:A Framework for Consensus Partitioning
Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to straightforward compare results. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It automatically generates detailed reports for the complete analysis. 6. It allows applying consensus partitioning in a hierarchical manner.
Maintained by Zuguang Gu. Last updated 2 months ago.
clusteringgeneexpressionclassificationsoftwareconsensus-clusteringcpp
61 stars 7.49 score 112 scripts
razrahman
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>.
Maintained by Raziur Rahman. Last updated 8 years ago.
2 stars 2.97 score 26 scripts 3 dependentsrazrahman
IntegratedMRF:Integrated Prediction using Uni-Variate and Multivariate Random Forests
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.
Maintained by Raziur Rahman. Last updated 7 years ago.
1.26 score 18 scripts