Boodd: An R Package for Bootstrap for Dependent Data

Bootstrap techniques are widely used in practice to approximate sampling distributions when analytical results are unavailable. The R package boodd, which accompanies the book Bootstrap for Dependent Data, with an R Package, provides a broad collection of bootstrap techniques not only for independent data but also for a wide class of dependent time series. In addition, package boodd offers a flexible framework for bootstrap inference, including, for example, the construction of confidence intervals, comparisons with normal approximations, comprehensive summary and plotting tools. In this paper, we provide an overview of the underlying methodological foundations, describe several innovative bootstrap methods implemented in the package, and illustrate their performance through selected examples.