Benjamin Baumgartner <benjamin.baumgartner@stat.unibe.ch>,
Riccardo Gatto <gatto@stat.unibe.ch>

Benjamin Baumgartner <benjamin@baumgrt.com>, Riccardo Gatto <gatto@stat.unibe.ch>
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.

A Bootstrap Test for the Probability of Ruin in the Classical
Risk Process
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.

Bootstrap Subsamplings of Sparse Partial Least Squares -
Discriminant Analysis for Classification and Signature
Identification
Florian Rohart [aut, cre], Kim-Anh Le Cao [boss], Christine Wells [boss]
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.

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.

Bootstrap for testing the hypothesis that the spectral densities of a number m, m>=2, not necessarily independent time series are equal

Bootstrap for testing the hypothesis that the spectral
densities of a number m, m>=2, not necessarily independent time
series are equal

Functions for the Book "An Introduction to the Bootstrap"
S original by Rob Tibshirani <tibs@utstat.toronto.edu>.
R port by Friedrich Leisch.
Software (bootstrap, cross-validation, jackknife) and
data for the book "An Introduction to the Bootstrap" by B. Efron
and R. Tibshirani, 1993, Chapman and Hall.