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.

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>.

Implements fast, exact bootstrap Principal Component Analysis and
    Singular Value Decompositions for high dimensional data, as described in
    <doi:10.1080/01621459.2015.1062383> (see also <arXiv:1405.0922> ). For data matrices that are too large to operate
    on in memory, users can input objects with class 'ff' (see the 'ff'
    package), where the actual data is stored on disk. In response, this
    package will implement a block matrix algebra procedure for calculating the
    principal components (PCs) and bootstrap PCs. Depending on options set by
    the user, the 'parallel' package can be used to parallelize the calculation of
    the bootstrap PCs.