---
output: github_document
---

<!-- README.md is generated from the source: README.Rmd -->

# bcaboot

<!-- [![Travis-CI Build Status](https://travis-ci.org/bnaras/bcaboot.svg?branch=master)](https://travis-ci.org/bnaras/bcaboot) -->
<!-- [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/bcaboot)](https://cran.r-project.org/package=bcaboot) -->
<!-- [![Coverage -->
<!-- Status](https://img.shields.io/codecov/c/github/bnaras/bcaboot/master.svg)](https://codecov.io/github/bnaras/bcaboot?branch=master) -->
<!-- [![](https://cranlogs.r-pkg.org/badges/bcaboot)](https://cloud.r-project.org/package=bcaboot) -->

Bootstrap confidence intervals depend on three elements:

- the cdf of the bootstrap replications
- the bias-correction number which depends on the proportion of
  bootstrap estimates that are less than the original estimate
- the acceleration number that measures the rate of
  change in standard deviation of the estimate as the data changes.

The first two of these depend only on the bootstrap distribution, and
not how it is generated: parametrically or
non-parametrically. Therefore, the only difference in a parametric bca
analysis would lie in the nonparametric estimation of the
acceleration, often a negligible error. 

The package `bcaboot` provides functions to compute bootstrap
confidence intervals in an (almost) automatic fashion. Further details
may be found in the paper by Efron and Narasimhan below.

## References

Efron, Bradley, and Balasubramanian Narasimhan. The Automatic
Construction of Bootstrap Confidence Intervals. (2018)