knitr, ggplot2 (>= 3.2.1), cowplot, automap (>= 1.0-14),
rgeos, rgdal, future, future.apply, shiny (>= 1.0.3),
shinydashboard, geosphere, methods, rmarkdown, testthat, covr

Block Forests: Random Forests for Blocks of Clinical and Omics
Covariate Data
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 (2018). The package includes four other random forest
variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 'VarProb', and 'SplitWeights'. These were
also considered in Hornung & Wright (2018), 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. (2018) Block Forests: random forests for blocks of clinical and
omics covariate data. Technical Report, Department of Statistics, University of Munich.
Rcpp (>= 0.11.2), Matrix, methods, survival

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 (2018). The package includes four
other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel',
'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2018),
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. (2018) Block Forests: random forests for
blocks of clinical and omics covariate data. Technical Report, Department of
Statistics, University of Munich.