Getting Started

Requirements and installation of dabest.

Introduction

DABEST is a package for Data Analysis with Bootstrapped ESTimation

Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment/intervention, as opposed to a false dichotomy engendered by P values.

An estimation plot has two key features.

  1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.
  2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes.

DABEST powers estimationstats.com, allowing everyone access to high-quality estimation plots.

Requirements

Python 3.10 is strongly recommended. DABEST has also been tested with Python 3.8 and onwards.

In addition, the following packages are also required (listed with their minimal versions):

To obtain these package dependencies easily, it is highly recommended to download the Anaconda distribution of Python.

Installation

  1. Using pip

At the command line, run

$ pip install dabest
  1. Using Github

Clone the DABEST-python repo locally (see instructions here).

Then, navigate to the cloned repo in the command line and run

$ pip install .

Testing

To test DABEST, you will need to install pytest and nbdev.

Run nbdev_export && nbdev_test in the root directory of the source distribution. This runs the value assertion tests in dabest/tests folder

Run pytest in the root directory of the source distribution. This runs the image-based tests in dabest/tests/mpl_image_tests sub folder.

The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.

Bugs

Please report any bugs on the Github issue tracker for DABEST-python.

Contributing

All contributions are welcome. Please fork the Github repo and open a pull request.