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modernBoot 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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.