Title: A Simple S3 Class for Representing Vectors of Binary Data
person("Hadley", "Wickham", , "hadley@rstudio.com", role = c("aut", "cre")),
Description: R's raw vector is useful for storing a single binary object.
What if you want to put a vector of them in a data frame? The blob
package provides the blob object, a list of raw vectors, suitable for

person("Kirill", "Müller", , "krlmlr+r@mailbox.org", role = c("cre")),

What if you want to put a vector of them in a data frame? The 'blob'
Suggests: covr, pillar (>= 1.2.1), testthat

Description: R's raw vector is useful for storing a single
binary object.  What if you want to put a vector of them in a data
frame? The 'blob' package provides the blob object, a list of raw
vectors, suitable for use as a column in data frame.
Imports: methods, prettyunits, rlang, vctrs (>= 0.2.0)
Suggests: covr, crayon, pillar (>= 1.2.1), testthat

Imports: methods, rlang, vctrs (>= 0.2.1)

person("Kirill", "Müller", , "krlmlr+r@mailbox.org", role = "cre"),

Title: Spatial and Environmental Blocking for K-Fold Cross-Validation
Authors@R: c(person("Roozbeh", "Valavi", role = c("aut", "cre"),
person("José", "Lahoz-Monfort", role = "aut",
person("Gurutzeta", "Guillera-Arroita", role = "aut",
Description: Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.
Imports: raster (>= 2.5-8), sf (>= 0.8-0), progress
Suggests: knitr, ggplot2 (>= 3.2.1), cowplot, automap (>= 1.0-14),
rgeos, future, future.apply, shiny (>= 1.0.3), shinydashboard,
geosphere, methods, rmarkdown, testthat, covr

rgeos, rgdal, future, future.apply, shiny (>= 1.0.3),
shinydashboard, geosphere, methods, rmarkdown, testthat, covr

Package: blockForest
Type: Package
Title: Block Forests: Random Forests for Blocks of Clinical and Omics
Covariate Data
Version: 0.1.7
Date: 2018-12-18
Author: Roman Hornung, Marvin N. Wright
Maintainer: Marvin N. Wright <cran@wrig.de>
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 (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.
License: GPL-3
Imports: Rcpp (>= 0.11.2), Matrix, methods, survival
LinkingTo: Rcpp, RcppEigen
Depends: R (>= 3.1)
Suggests: testthat
RoxygenNote: 6.1.0
NeedsCompilation: yes
Packaged: 2018-12-18 11:41:08 UTC; wright
Repository: CRAN
Date/Publication: 2018-12-30 17:20:12 UTC