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>.

Identifies genome-related molecular traits with significant 
  evidence of genetic regulation and performs a bootstrap procedure to 
  correct estimated effect sizes for over-estimation present in cis-QTL
  mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 
  2018 <doi: 10.1093/nar/gky780>.

Bootstrap resampling methods have been widely studied in the context of survey data. This package implements various bootstrap resampling techniques tailored for survey data, with a focus on stratified simple random sampling and stratified two-stage cluster sampling. It provides tools for precise and consistent bootstrap variance estimation for population totals, means, and quartiles. Additionally, it enables easy generation of bootstrap samples for in-depth analysis.