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blockForest
Block Forests: Random Forests for Blocks of Clinical and Omics Covariate Data
View on CRAN: Click
here
Download and install blockForest package within the R console
Install from CRAN:
install.packages("blockForest")
Install from Github:
library("remotes")
install_github("cran/blockForest") Install by package version:
library("remotes")
install_version("blockForest", "0.2.6") Attach the package and use:
library("blockForest")
Maintained by
Marvin N. Wright
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All associated links for this package
10.32614/CRAN.package.blockForest . https://github.com/bips-hb/blockForest/issues . https://github.com/bips-hb/blockForest . blockForest results . blockForest.pdf . blockForest_0.2.6.tar.gz . blockForest_0.2.6.zip . blockForest_0.2.6.zip . blockForest_0.2.6.zip . blockForest_0.2.6.tgz . blockForest_0.2.6.tgz . blockForest_0.2.6.tgz . blockForest_0.2.6.tgz . blockForest_0.2.6.tgz . blockForest_0.2.6.tgz . blockForest archive . https://CRAN.R-project.org/package=blockForest .
First Published: 2018-12-30
Latest Update: 2023-03-31
Description:
A random forest variant 'block forest' ('BlockForest') tailored to the
prediction of binary, survival and continuous outcomes using block-structured
covariate data, for example, clinical covariates plus measurements of a certain
omics data type or multi-omics data, that is, data for which measurements of
different types of omics data and/or clinical data for each patient exist. Examples
of different omics data types include gene expression measurements, mutation data
and copy number variation measurements.
Block forest are presented in Hornung & Wright (2019). The package includes four
other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel',
'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019),
but performed worse than block forest in their comparison study based on 20 real
multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest')
in applications. The other random forest variants can, however, be consulted for
academic purposes, for example, in the context of further methodological
developments.
Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. .
How to cite:
Marvin N. Wright (2018). blockForest: Block Forests: Random Forests for Blocks of Clinical and Omics Covariate Data. R package version 0.2.6, https://cran.r-project.org/web/packages/blockForest. Accessed 29 Mar. 2025.
Previous versions and publish date:
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Complete documentation for blockForest
Functions, R codes and Examples using
the blockForest R package
Some associated functions: blockForest . blockfor . predict.blockForest . predictions.blockForest . predictions.blockForest.prediction . timepoints.blockForest . timepoints.blockForest.prediction . treeInfo .
Some associated R codes: CvalueOptimizer.R . CvalueOptimizerClassification.R . CvalueOptimizerRegression.R . CvalueOptimizerSurvival.R . Data.R . RcppExports.R . blockForest.R . blockfor.R . formula.R . getTerminalNodeIDs.R . importance.R . infinitesimalJackknife.R . oob_error.R . predict.R . predictions.R . print.R . timepoints.R . treeInfo.R . utility.R . Full blockForest package functions and examples
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