Modern Resampling Methods: Bootstraps, Wild, Block, Permutation, and Selection Guidance
Automatic Resampling Method Selection
Bias-Corrected and Accelerated (BCa) Bootstrap Confidence Interval
Nonparametric Bootstrap Confidence Interval for the Mean
Simulation Study: Compare Bootstrap Methods
Moving Block Bootstrap for Time Series
Permutation maxT for Multiple Testing
Two-Sample Permutation Test
Stationary Bootstrap (Politis & Romano)
Studentized Bootstrap Confidence Interval for Quantiles
Wild Bootstrap for Linear Model Coefficients
Implements modern resampling and permutation methods for robust statistical inference without restrictive parametric assumptions. Provides bias-corrected and accelerated (BCa) bootstrap (Efron and Tibshirani (1993) <doi:10.1201/9780429246593>), wild bootstrap for heteroscedastic regression (Liu (1988) <doi:10.1214/aos/1176351062>, Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>), block bootstrap for time series (Politis and Romano (1994) <doi:10.1080/01621459.1994.10476870>), and permutation-based multiple testing correction (Westfall and Young (1993) <ISBN:0-471-55761-7>). Methods handle non-normal data, heteroscedasticity, time series correlation, and multiple comparisons.