c(person("Claude", "Renaux", role = c("aut", "cre"),
email = "renaux@stat.math.ethz.ch"),
person("Peter", "Bühlmann", role = c("ths")))
Implementation of hierarchical inference based on Renaux and
Bühlmann (2021) 'Efficient Multiple Testing Adjustment for Hierarchical
Inference' <arXiv:2104.15028>.
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 an efficient 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.
glmnet, hdi, methods, parallel, stats, SIHR
Claude Renaux [aut, cre],
Peter Bühlmann [ths]

Implementation of hierarchical inference based on Renaux and
Bühlmann (2021) 'Efficient Multiple Testing Adjustment for Hierarchical
Inference' <arXiv:2104.15028>.
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 an efficient 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.

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.