statistics-resampling-online

Try the statistics-resampling package online in a Notebook with an Octave kernel within Jupyter Lab using Binder by clicking a launch-binder button below:

(default binder branch: master)

(stable binder branch: jammy)

(stable docker branch: jammy-docker) [recommended]

Note that the first time you access a statistics-resampling-online binder branch (since the last commit at the GitHub repository) it may take a while to build, but frequent access to statistics-resampling-online thereon should give load times less than a minute. Binder for the stable docker branches loads much faster and is more stable. Data files (.tsv and .csv) can be conveniently modified using the jupyterlab-spreadsheet-editor included in this environment. Other Jupyter Lab extensions included enable the real-time collaboration and the creation and editing of diagrams and Markdown documents. The environment also includes kernels to run workbooks in Julia, Python and R (in which additional packages and modules can be installed with Pkg, pip, or from CRAN respectively). Preferably, pre-built packages can also be installed from within code cells of a notebook by executing the system command !conda install -qc conda-forge <package-name>, where <package-name> is replaced with the name of a conda-forge package.

You could consider loading Jupyter notebooks and data files from your own GitHub repositories using this Binder environment with the help of the nbgitpuller link generator. This Binder repository is nbgitpuller enabled, so you can use it's environment (and it's image if it exists already) to load content (e.g. notebooks and data) from your own GitHub repository (for an example, see ). We recommend using one of the stable branches (e.g. jammy-docker) if you intend to use nbgitpuller. Alternatively, you could fork this repository or use it as a template for you own GitHub repository.

If you wish to download and use the package offline, you can find the package source code on GitHub. The documentation for the statistics-resampling package can be found at the following link:

Please cite the following in any publication that uses this resource:

• Penn, Andrew Charles. (2020). Resampling methods for small samples or samples with complex dependence structures. Zenodo. https://doi.org/10.5281/zenodo.3992392