gtools, corpcor, IsingSampler, mvtnorm, abind, Matrix, snow,
mgm (>= 1.2), NetworkToolbox (>= 1.1.0), pbapply, networktools,
Suggests: glasso, GGMncv, BDgraph, graphicalVAR, relaimpo, lavaan,

Title: A bootstrap test for the probability of ruin in the classical
Description: This package provides a framework for testing the
probability of ruin in the classical (compound Poisson) risk
process. It also includes some procedures for assessing and
comparing the performance between the bootstrap test and the

Author: Benjamin Baumgartner <benjamin@baumgrt.com>, Riccardo Gatto <gatto@stat.unibe.ch>
Description: This package provides a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality.

Title: A Bootstrap Test for the Probability of Ruin in the Classical
Description: We provide a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality.

Title: Bootstrap Subsamplings of Sparse Partial Least Squares -
Discriminant Analysis for Classification and Signature
Author: Florian Rohart [aut, cre], Kim-Anh Le Cao [boss], Christine Wells [boss]
Description: Bootstrap Subsamplings of sparse Partial Least Squares - Discriminant Analysis (sPLS-DA) for Classification and Signature Identification. The method is applicable to any classification problem with more than 2 classes. It relies on bootstrap subsamplings of sPLS-DA and provides tools to select the most stable variables (defined as the ones consistently selected over the bootstrap subsamplings) and to predict the class of test samples.

Description: Applicable to any classification problem with more than 2 classes. It relies on bootstrap subsamplings of sPLS-DA and provides tools to select the most stable variables (defined as the ones consistently selected over the bootstrap subsamplings) and to predict the class of test samples.