Lisp-Stat Statistics

A consolidation of Common Lisp statistics libraries
Explore the docs »

Report Bug · Request Feature · Reference Manual

Table of Contents

1. About the Project
2. Installation
3. Usage
4. Functions
5. Roadmap
6. Resources
7. Contributing
8. License
9. Contact

About the Project

This repository contains two main system definitions for statistics, streaming and batch. batch is the kind of implementation you probably think of first when working with statistics in R, Python or Julia. All data is loaded into memory and then analysed.

streaming is based for the most part on the work of Bennett, J. et al. (2009). Numerically stable, single-pass, parallel statistics algorithms. IEEE Cluster Computing and Workshops, pp. 1-8. and West, D.H.D. (1979). Updating mean and variance estimates: An improved method. Communications of the ACM, 22(9), pp. 532-535. These are high quality, stable numerical statistical algorithms that are somewhat unusual in statistical libraries. These are more complete than batch and often we'll turn a matrix into a stream and then compute the descriptive statistic.

This means that if you want to contribute to batch, it's more or less a green field at the moment.

Both of these depend on an externalized stats-generic system that holds the generic definiton of things like mean. The reason for this is that these terms are overloaded. We might want the mean of a vector, matrix, data-frame or stream so the generics live in a separate system.

Installation

To get a local copy up and running follow these steps:

(ql:quickload :statistics)

or

(asdf:load-system :statistics/batch)
(asdf:load-system :statistics/streaming)

If you already have the system downloaded to your local machine.

Usage

Create a data frame of weather data:

(load #P"LS:DATA;sg-weather")

and take the mean maximum temperature:

LS-USER> (mean sg-weather:max-temps)

For more examples, please refer to the Documentation.

Resources

This system is part of the Lisp-Stat project; that should be your first stop for information. Also see the resources and community pages for more information.

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated. Please see CONTRIBUTING for details on the code of conduct and the process for submitting pull requests.

Licenses

• Lisp-Stat: Microsoft Public License. See LICENSE

Contact

Project Link: https://github.com/lisp-stat/statistics