README.Rmd

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# symbootpkg

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The goal of symbootpkg is to provide flexible tools to compute bootstrap-t, equal-tailed, and symmetric confidence intervals for statistical analysis. It incorporates methods for plugin and nested bootstrap standard error estimations, with options for parallelization to improve efficiency.

## Features
- **Bootstrap-t Confidence Intervals**: Compute confidence intervals using the bootstrap-t method with options for plugin and nested standard error estimates.
- **Equal-Tailed and Symmetric Intervals**: Includes methods for both equal-tailed and symmetric bootstrap confidence intervals.
- **Flexible Standard Error Functions**: Supports user-specified `sdfun` functions or nested bootstrap for standard error estimation.
- **Parallel Processing**: Optional parallelization to leverage multiple cores for faster computation.


## Installation

You can install the development version of symbootpkg from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("blessingofori-atta/symbootpkg")
```

## Usage

### Basic Example

Here's a basic example of how to use the bootstrap and bootstrap_t functions:

```{r example}
library(symbootpkg)
set.seed(123)

# Define a statistic function
beta <- function(dat) {
  mod <- lm(Sepal.Length ~ Sepal.Width, data = dat)
  bb <- mod$coefficients[2]
  return(bb)
}

# Perform bootstrap resampling
boot_obj <- bootstrap(
  data = iris,
  statistic = beta,
  nboot = 1000,
  return_samples = TRUE
)

# Compute symmetic bootstrap-t confidence intervals
ci_result <- bootstrap_t(
  boot_obj = boot_obj,
  Bsd = 25,
  method = "bootsym-nested",
  alpha = 0.05
)

# Print results
print(ci_result)
```


### Parallel Processing Example

You can enable parallel processing to speed up the computation:

```{r cars}
ci_result_parallel <- bootstrap_t(
  boot_obj = boot_obj,
  Bsd = 25,
  method = "bootsym-nested",
  parallel = TRUE,
  cores = 2,
  alpha = 0.05
)

# Print results
print(ci_result_parallel)
```


## Contribution

Contributions are welcome! Please open an issues or submit a pull request to improve the package.

## License

This package is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## References

1. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York:Chapman & Hall. 
2. Hall, P. (1988). On Symmetric Bootstrap Confidence Intervals. Journal of the Royal Statistical Society. Series B (Methodological), 50(1), 35–45. http://www.jstor.org/stable/2345806.
3. Elias, C. J. (2015). Percentile and percentile- t bootstrap confidence intervals: A practical comparison. Journal of Econometric Methods, 4(1), 153–161. doi:10.1515/jem-2013-0015
4. http://users.stat.umn.edu/~helwig/notes/npboot-notes.html


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