https://github.com/james-montgomery/lind
https://github.com/james-montgomery/lind
analysis bayesian-statistics design experiments frequentist-statistics statistics testing
Last synced: 9 months ago
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- Host: GitHub
- URL: https://github.com/james-montgomery/lind
- Owner: James-Montgomery
- License: other
- Created: 2020-08-22T17:43:22.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-04-27T17:12:27.000Z (about 5 years ago)
- Last Synced: 2025-06-04T18:09:52.828Z (about 1 year ago)
- Topics: analysis, bayesian-statistics, design, experiments, frequentist-statistics, statistics, testing
- Language: Python
- Homepage: https://james-montgomery.github.io/lind/build/html/index.html
- Size: 6.49 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Lind
This package is meant to house common tools used in experiments design and
analysis. There is little "novel" functionality here, but it is hopefully
packaged in a way that is convenient and useful for users.
I began this package for two reasons. First, I was dissatisfied with the
existing packages available for experimentation in python. They seemed like
collections of random tools rather than a cohesive set of utilities that work
together in harmony to a united purpose. Second, I used this as an opportunity
to refresh my understanding of various statistical tools and methods.
### Authors
**James Montgomery** - *Initial Work* - [jamesmontgomery.us](http://jamesmontgomery.us)
### License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
### About the Name
Looking for a name for this package I tried looking back into the history of
experimentation. I was tempted to name the package after King Nebuchadnezzar in
reference to the "legumes and water" anecdote from the book of Daniel. This is
often considered one of the earliest controlled "trials".
However, some of the first modern controlled trials were conducted by Dr. James
Lind. There are many scatter references to trials throughout history, but Lind
represented the start of the modern era of controlled trials and their
integration into the scientific method. Hence I named the package after Lind.
If you have a chance, I recommend taking an afternoon and reading about the
work Lind did to fight the disease Scurvy.
## Getting Started
These instructions will get you a copy of the project up and running on your
local machine for development and testing purposes.
### Installing
For a local installation, first git clone this repository. Then follow these
instructions:
```
pip install .
```
To install from [pypi](https://pypi.org/project/lind/):
```
pip install lind
```
To install the package with test dependencies add `[tests]` to the install
command:
```
pip install lind[tests]
```
To install with test dependencies and R backends:
```
pip install -U "lind[tests, r_backends]"
```
Some functionality requires pre-computed designs available as static files. To
install with static file support:
```
pip install -U "lind[tests, r_backends, static_designs]"
```
### R Backends
Many of the best experiment design packages are written in R due to the
language's popularity in academia. However, R is not always a convenient
language to work with (especially for industry practitioners). If you install
lind with the `r_backends` extra requirement, you will get access to additional
functionality drawing from popular R experimental design package. The default
installation relies only on python native code.
**Warning:** We have chosen respected and reputable R packages to use as our
backend where R code is used. However, code quality and accuracy of backend
R code is not tested in this package. Please see the documentation for those
packages to learn more about them. R package name are documented in the
appropriate module docstrings.
### Quick Start
TODO
## Testing
Testing is an important part of creating maintainable, production grade code.
Below are instructions for running unit and style tests as well as installing
the necessary testing packages. Tests have intentionally been separated from
the installable pypi package for a variety of reasons.
Make sure you have the required testing packages:
```
pip install -r requirements_test.txt
```
To install the project with test dependencies see the install section.
### Running the unit tests
We use the pytest framework for unit testing. Test preset args are defined
in `pytest.ini`.
```
pytest
```
We aspire to no lower than 80% code coverage for unit tests.
### Running the style tests
Having neat and legible code is important. Having documentation is also
important. We use pylint as our style guide framework. Many of our naming
conventions follow directly from the literary sources they come from. This
makes it easier to read the mathematical equations and see how they translate
into the code. This sometimes forces us to break pep8 conventions for naming.
Linting presets are defined in pylintrc.
```
pylint lind
```
We aspire to no lower than an 8.0 / 10.0 style score when linting.
## Contributor's Guide
Here are some basic guidelines for contributing.
### Branch Strategy
This repository doesn't use a complicated branching strategy. Simply create a
feature branch off of master. When the feature is ready to be integrated with
master, submit a pull request. A pull request will re quire at least one peer
review and approval from the repository owner.
### Style Guide
Please stick to pep8 standards when for your code. Use numpy style docstrings.
### Test Requirements
Please use pytest as your testing suite. You code should have >= 80% coverage.
### Updating the Docs
Updating the documentation is simple. First, let auto-docs check for updates to
the package structure.
```
cd docs
make html
```
## Acknowledgments
A big thanks to Mack Sweeney, Tom Caputo, and Matt Van Adlesberg, each of which
has put up with my many questions about experimental design and analysis. A
special thanks to Mack Sweeney who continues to challenge me to become a better
software engineer.
## TODO
1. Install R in docker containers
## Useful Resources
Many of the best packages for experimental design are written in R. The link
below is a comprehensive survey of useful DOE (Design of Experiments) packages
in R: [LINK](https://cran.r-project.org/web/views/ExperimentalDesign.html).