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 <doi:10.48550/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.

Set of functions to perform various bootstrap unit root tests for both individual time series
  (including augmented Dickey-Fuller test and union tests), multiple time series and panel data; see
  Smeekes and Wilms (2023) <doi:10.18637/jss.v106.i12>,
  Palm, Smeekes and Urbain (2008) <doi:10.1111/j.1467-9892.2007.00565.x>,
  Palm, Smeekes and Urbain (2011) <doi:10.1016/j.jeconom.2010.11.010>, 
  Moon and Perron (2012) <doi:10.1016/j.jeconom.2012.01.008>, 
  Smeekes and Taylor (2012) <doi:10.1017/S0266466611000387> and 
  Smeekes (2015) <doi:10.1111/jtsa.12110> for key references.

The card game War is simple in its rules but can be lengthy. In
    another domain, the nonparametric bootstrap test with pooled resampling
    (nbpr) methods, as outlined in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>,
    is optimal for comparing paired or unpaired means in non-normal data,
    especially for small sample size studies. However, many researchers are
    unfamiliar with these methods. The 'bootwar' package bridges this gap by
    enabling users to grasp the concepts of nbpr via Boot War, a variation of the
    card game War designed for small samples. The package provides functions like
    score_keeper() and play_round() to streamline gameplay and scoring. Once a
    predetermined number of rounds concludes, users can employ the analyze_game()
    function to derive game results. This function leverages the 'npboottprm'
    package's nonparboot() to report nbpr results and, for comparative analysis,
    also reports results from the 'stats' package's t.test() function. Additionally,
    'bootwar' features an interactive 'shiny' web application, bootwar(). This
    offers a user-centric interface to experience Boot War, enhancing understanding
    of nbpr methods across various distributions, sample sizes, number of bootstrap
    resamples, and confidence intervals.