Title: Set Data Visualization Using Hierarchies
Description: Pure set data visualization approaches are often limited in
scalability due to the combinatorial explosion of distinct set families as
the number of sets under investigation increases. hierarchicalSets applies
a set centric hierarchical clustering of the sets under investigation and
uses this hierarchy as a basis for a range of scalable visual
representations. hierarchicalSets is especially well suited for collections
of sets that describe comparable comparable entities as it relies on the
sets to have a meaningful relational structure.
Imports: ggdendro, ggplot2 (>= 2.0.0), stats, Rcpp, scales, Matrix,
MASS, RColorBrewer, gtable, grDevices, methods, utils

Title: Convex Banding of the Covariance Matrix
Description: Implementation of the convex banding procedure (using a
hierarchical group lasso penalty) for covariance estimation that is
introduced in Bien, Bunea, Xiao (2015) Convex Banding of the Covariance
Matrix. Accepted for publication in JASA.
Author: Jacob Bien <jbien@cornell.edu>, Florentina Bunea, and Luo Xiao

Title: Enabling Hierarchical Multiple Testing
Authors@R: c(person("Claude", "Renaux", role = c("aut", "cre"),
person("Peter", "Bühlmann", role = c("ths")))
Description: Implementation of hierarchical inference based on Renaux and
Bühlmann (2021) 'Efficient Multiple Testing Adjustment for Hierarchical
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
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.
Imports: glmnet, hdi, methods, parallel, stats, SIHR