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Random resampling with replacement (`BasicSampling`)\n  - Antithetic resampling, introducing negative correlation between samples (`AntitheticSampling`)\n  - Balanced random resampling, reducing bias (`BalancedSampling`)\n  - Exact resampling, iterating through all unique resamples (`ExactSampling`):\n    deterministic bootstrap, suited for small samples sizes\n  - Resampling of residuals in generalized linear models (`ResidualSampling`, `WildSampling`)\n  - Maximum Entropy bootstrapping for dependent and non-stationary datasets (`MaximumEntropySampling`)\n\n- Confidence intervals:\n  - Basic (`BasicConfInt`)\n  - Percentile (`PercentileConfInt`)\n  - Normal distribution (`NormalConfInt`)\n  - Studendized (`StudentConfInt`)\n  - Bias-corrected and accelerated (BCa) (`BCaConfInt`)\n\n\n## Installation\n\nThe `Bootstrap` package is part of the Julia ecosphere and the latest release\nversion can be installed with\n\n```julia\nusing Pkg\nPkg.add(\"Bootstrap\")\n```\n\nMore details on packages and how to manage them can be found in the [package\nsection](https://docs.julialang.org/en/v1/stdlib/Pkg/) of the Julia\ndocumentation.\n\n\n## Examples\n\nThis example illustrates the basic usage and cornerstone functions of the package.\nMore elaborate cases are covered in the documentation notebooks.\n\n```julia\n  using Bootstrap\n```\n\nOur observations in `some_data` are sampled from a standard normal distribution.\n\n```julia\n  some_data = randn(100);\n```\n\nLet's bootstrap the standard deviation (`std`) of our data, based on 1000\nresamples and with different bootstrapping approaches.\n\n```julia\n  using Statistics  # the `std` methods live here\n  \n  n_boot = 1000\n\n  ## basic bootstrap\n  bs1 = bootstrap(std, some_data, BasicSampling(n_boot))\n\n  ## balanced bootstrap\n  bs2 = bootstrap(std, some_data, BalancedSampling(n_boot))\n```\n\nWe can explore the properties of the bootstrapped samples, for example, the\nestimated bias and standard error of our statistic.\n\n```julia\n  bias(bs1)\n  stderror(bs1)\n```\n\nFurthermore, we can estimate confidence intervals (CIs) for our statistic of\ninterest, based on the bootstrapped samples. `confint` returns a `Tuple` of `Tuples`,\nwhere each `Tuple` is of the form `(statistic_value, upper_confidence_bound, lower_confidence_bound)`.\nA confidence interval is returned for each variable in the bootstrap model.\n\n```julia\n  ## calculate 95% confidence intervals\n  cil = 0.95;\n\n  ## basic CI\n  bci1 = confint(bs1, BasicConfInt(cil));\n\n  ## percentile CI\n  bci2 = confint(bs1, PercentileConfInt(cil));\n\n  ## BCa CI\n  bci3 = confint(bs1, BCaConfInt(cil));\n\n  ## Normal CI\n  bci4 = confint(bs1, NormalConfInt(cil));\n```\n\n\n## References\n\nThe [bootstrapping wikipedia article](https://en.wikipedia.org/wiki/Bootstrapping_(statistics))\nis a comprehensive introduction into the topic.  An extensive description of the\nbootstrap is the focus of the book *Davison and Hinkley (1997):\n[Bootstrap Methods and Their Application](http://statwww.epfl.ch/davison/BMA/)*.\nMost of the methodology covered in the book is implemented in the\n[boot](https://cran.r-project.org/web/packages/boot/index.html) package for the\n[R programming language](https://www.r-project.org/). [More references](docs/src/references.md)\nare listed in the documentation for further reading.\n\n\n## Contributions and Feedback\n\nContributions of any kind are very welcome. Please feel free to open pull\nrequests or issues if you have suggestions for changes, ideas or questions.\n\n\n## Package Status\n\n[![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliangehring.github.io/Bootstrap.jl/stable)\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.596080.svg)](https://doi.org/10.5281/zenodo.596080)\n\n![Testing](https://github.com/juliangehring/Bootstrap.jl/workflows/Testing/badge.svg)\n\n[![Coverage](https://codecov.io/gh/juliangehring/Bootstrap.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/juliangehring/Bootstrap.jl)\n\nThe package uses [semantic versioning](https://semver.org/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjuliangehring%2Fbootstrap.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjuliangehring%2Fbootstrap.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjuliangehring%2Fbootstrap.jl/lists"}