Changes in version 0.1.0                        

New Features

  - Initial release of modernBoot package with core resampling methods
  - Bootstrap methods: bs_mean(), bca_ci(), studentized_ci() for
    confidence intervals
  - Wild bootstrap: wild_boot_lm() for heteroscedastic linear models
    with Rademacher and Mammen weights
  - Block bootstrap: moving_block_boot() and stationary_boot() for
    dependent time series data
  - Permutation tests: perm_test_2sample() for two-sample inference
  - Multiple testing correction: perm_maxT() for controlling family-wise
    error rate
  - Automated method selection: auto_select_method() intelligently
    recommends resampling approach based on data structure
  - Simulation tools: compare_methods_sim() for benchmarking different
    resampling methods
  - Parallel computation: Full support for future package
    parallelization (user-controlled)

Functions

Bootstrap Functions

  - bs_mean() — Nonparametric bootstrap confidence interval for the mean
    (percentile method)
  - bca_ci() — Bias-corrected and accelerated (BCa) bootstrap confidence
    interval
  - studentized_ci() — Studentized bootstrap confidence interval for
    quantiles

Dependent Data Bootstrap

  - moving_block_boot() — Moving block bootstrap for time series
  - stationary_boot() — Stationary bootstrap (Politis & Romano, 1994)

Model-Based Bootstrap

  - wild_boot_lm() — Wild bootstrap for linear regression with
    heteroscedasticity

Permutation Tests

  - perm_test_2sample() — Two-sample permutation test
  - perm_maxT() — Permutation maxT for multiple hypothesis testing with
    FWER control

Utilities

  - auto_select_method() — Automatic resampling method selection
  - compare_methods_sim() — Simulation comparison of bootstrap methods
  - Internal helpers: check_numeric_vector(), .safe_sample()

Documentation

  - Comprehensive package vignette: vignette("method-selection")
  - Full function documentation with examples
  - README with quick-start guide and installation instructions
  - CI/CD workflows (R-CMD-check on macOS, Linux, Windows; pkgdown
    deployment)

Dependencies

  - Imports: stats, utils, boot, future, future.apply
  - Suggests: testthat, covr, pkgdown, knitr, rmarkdown, rhub
  - Requires: R ≥ 4.0

Testing

  - Initial test suite included in tests/testthat/
  - Tests for bs_mean() and perm_test_2sample()
  - Comprehensive test expansion planned for future releases

References

  - Efron, B., & Tibshirani, R. J. (1993). An introduction to the
    bootstrap. Chapman and Hall/CRC.
  - Politis, D. N., & Romano, J. P. (1994). The stationary bootstrap.
    Journal of the American Statistical Association, 89(428), 1303-1313.
  - Wu, C. F. (1986). Jackknife, bootstrap and other resampling methods
    in regression analysis. Annals of Statistics, 14(4), 1261-1295.

Future Enhancements

  - Cluster bootstrap for clustered data
  - Bayesian bootstrap methods
  - Rcpp implementations for speed optimization
  - Extended vignettes with real-world examples
  - Performance benchmarking suite