Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', <doi:10.3389/fams.2021.693126>).

Contains functions for bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series.

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.

Provides a streamlined and user-friendly framework for
    bootstrapping in state space models, particularly when the number of
    subjects/units (n) exceeds one, a scenario commonly encountered in
    social and behavioral sciences. The parametric bootstrap implemented
    here was developed and applied in Pesigan, Russell, and Chow (2025)
    <doi:10.1037/met0000779>.

Model selection by bootstrapping the stepAIC() procedure.

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. This package is
        primarily provided for projects already based on it, and for
        support of the book. New projects should preferentially use the
        recommended package "boot".

Finite Population bootstrap algorithms to estimate the variance
    of the Horvitz-Thompson estimator for single-stage sampling. 
    For a survey of bootstrap methods for finite populations, see Mashreghi et Al. (2016) <doi:10.1214/16-SS113>.