The R-package 'hierbase' offers tools to perform hierarchical inference

Package: hierbase
Type: Package
Title: Enabling Hierarchical Multiple Testing
Version: 0.1.1
Authors@R: c(person("Claude", "Renaux", role = c("aut", "cre"),
email = "renaux@stat.math.ethz.ch"),
person("Peter", "Bühlmann", role = c("ths")))
Description: Implementation of hierarchical inference based on Meinshausen (2008).
Hierarchical testing of variable importance. Biometrika, 95(2), 265-278 and
Renaux, Buzdugan, Kalisch, and Bühlmann, (2020). Hierarchical inference for
genome-wide association studies: a view on methodology with software.
Computational Statistics, 35(1), 1-40.
The R-package 'hierbase' offers tools to perform hierarchical inference
for one or multiple data sets based on ready-to-use (group) test functions
or alternatively a user specified (group) test function.
The procedure is based on a hierarchical multiple testing
correction and controls the family-wise error rate (FWER).
The functions can easily be run in parallel.
Hierarchical inference can be applied to (low- or) high-dimensional
data sets to find significant groups or single variables (depending on
the signal strength and correlation structure) in a data-driven and
automated procedure. Possible applications can for example be found
in statistical genetics and statistical genomics.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.0.0)
Imports: glmnet, hdi, methods, parallel, stats, SIHR
Suggests: knitr, MASS, testthat
VignetteBuilder: knitr
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-11-03 13:19:37 UTC; renauxc
Author: Claude Renaux [aut, cre],
Peter Bühlmann [ths]
Maintainer: Claude Renaux <renaux@stat.math.ethz.ch>
Repository: CRAN
Date/Publication: 2021-11-04 00:30:02 UTC

Package: hierbase
Type: Package
Title: Enabling Hierarchical Multiple Testing
Version: 0.1.2
Authors@R: c(person("Claude", "Renaux", role = c("aut", "cre"),
email = "renaux@stat.math.ethz.ch"),
person("Peter", "Bühlmann", role = c("ths")))
Description: Implementation of hierarchical inference based on Meinshausen (2008).
Hierarchical testing of variable importance. Biometrika, 95(2), 265-278 and
Renaux, Buzdugan, Kalisch, and Bühlmann, (2020). Hierarchical inference for
genome-wide association studies: a view on methodology with software.
Computational Statistics, 35(1), 1-40.
The R-package 'hierbase' offers tools to perform hierarchical inference
for one or multiple data sets based on ready-to-use (group) test functions
or alternatively a user specified (group) test function.
The procedure is based on a hierarchical multiple testing
correction and controls the family-wise error rate (FWER).
The functions can easily be run in parallel.
Hierarchical inference can be applied to (low- or) high-dimensional
data sets to find significant groups or single variables (depending on
the signal strength and correlation structure) in a data-driven and
automated procedure. Possible applications can for example be found
in statistical genetics and statistical genomics.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.0.0)
Imports: glmnet, hdi, methods, parallel, stats, SIHR
Suggests: knitr, MASS, testthat
VignetteBuilder: knitr
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-11-08 10:32:19 UTC; renauxc
Author: Claude Renaux [aut, cre],
Peter Bühlmann [ths]
Maintainer: Claude Renaux <renaux@stat.math.ethz.ch>
Repository: CRAN
Date/Publication: 2021-11-08 12:00:02 UTC