5 packages on CRAN
1 packages on GitHub
Integrative framework for the simultaneous estimation of interactions from different class of data.
This is an implementation of Leo Breiman's and Adele Cutler's Random Forest algorithms for classification and regression, with optimizations for performance and for handling of data sets that are too large to be processed in memory. Forests can be built in parallel at two levels. First, trees can be grown in parallel on a single machine using foreach. Second, multiple forests can be built in parallel on multiple machines, then merged into one. For large data sets, disk-based big.matrix's may be used for storing data and intermediate computations, to prevent excessive virtual memory swapping by the operating system. Currently, only classification forests with a subset of the functionality in Breiman and Cutler's original code are implemented. More functionality and regression trees may be added in the future.
Provides a flexible integrative algorithm that allows information from prior data, such as protein protein interactions and gene knock-down, to be jointly considered for gene regulatory network inference.
Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.
Feature Selection with Regularized Random Forest. This package is based on the 'randomForest' package by Andy Liaw. The key difference is the RRF() function that builds a regularized random forest.
A modification of Breiman and Cutler's classification random forests modified for SNP (Single Nucleotide Polymorphism) data (based on randomForest v4.6-7) to prevent X-chromosome SNP variable importance bias compared to autosomal SNPs by simulating the process of X chromosome inactivation. Classification is based on a forest of trees using random subsets of SNPs and other variables as inputs.