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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

About

Statistical functions in Common Lisp

lisp-stat.github.io/statistics

Topics

common-lispstatistics

Resources

Readme
MS-PL license

Contributing

Contributing
Activity
Custom properties

Stars

5 stars

Watchers

3 watching

Forks

4 forks
Report repository

Contributors

Languages

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GitHub - Lisp-Stat/statistics: Statistical functions in Common Lisp · GitHub
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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
About
Statistical functions in Common Lisp
lisp-stat.github.io/statistics
Topics
common-lispstatistics
Resources
Readme
MS-PL license
Contributing
Contributing
Activity
Custom properties
Stars
5 stars
Watchers
3 watching
Forks
4 forks
Report repository
Contributors
Languages

, 'i');
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GitHub - Lisp-Stat/statistics: Statistical functions in Common Lisp · GitHub
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ContributorsForksStargazersIssuesMS-PL LicenseLinkedIn

Logo
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
About
Statistical functions in Common Lisp
lisp-stat.github.io/statistics
Topics
common-lispstatistics
Resources
Readme
MS-PL license
Contributing
Contributing
Activity
Custom properties
Stars
5 stars
Watchers
3 watching
Forks
4 forks
Report repository
Contributors
Languages

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GitHub - Lisp-Stat/statistics: Statistical functions in Common Lisp · GitHub
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ContributorsForksStargazersIssuesMS-PL LicenseLinkedIn

Logo
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
About
Statistical functions in Common Lisp
lisp-stat.github.io/statistics
Topics
common-lispstatistics
Resources
Readme
MS-PL license
Contributing
Contributing
Activity
Custom properties
Stars
5 stars
Watchers
3 watching
Forks
4 forks
Report repository
Contributors
Languages

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GitHub - Lisp-Stat/statistics: Statistical functions in Common Lisp · GitHub
Skip to content
BranchesTags
Repository files navigation
ContributorsForksStargazersIssuesMS-PL LicenseLinkedIn

Logo
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
About
Statistical functions in Common Lisp
lisp-stat.github.io/statistics
Topics
common-lispstatistics
Resources
Readme
MS-PL license
Contributing
Contributing
Activity
Custom properties
Stars
5 stars
Watchers
3 watching
Forks
4 forks
Report repository
Contributors
Languages

, 'i');
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GitHub - Lisp-Stat/statistics: Statistical functions in Common Lisp · GitHub
Skip to content
BranchesTags
Repository files navigation
ContributorsForksStargazersIssuesMS-PL LicenseLinkedIn

Logo
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
About
Statistical functions in Common Lisp
lisp-stat.github.io/statistics
Topics
common-lispstatistics
Resources
Readme
MS-PL license
Contributing
Contributing
Activity
Custom properties
Stars
5 stars
Watchers
3 watching
Forks
4 forks
Report repository
Contributors
Languages

, 'i');
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GitHub - Lisp-Stat/statistics: Statistical functions in Common Lisp · GitHub
Skip to content
BranchesTags
Repository files navigation
ContributorsForksStargazersIssuesMS-PL LicenseLinkedIn

Logo
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
About
Statistical functions in Common Lisp
lisp-stat.github.io/statistics
Topics
common-lispstatistics
Resources
Readme
MS-PL license
Contributing
Contributing
Activity
Custom properties
Stars
5 stars
Watchers
3 watching
Forks
4 forks
Report repository
Contributors
Languages

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filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
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filter: invert(1) hue-rotate(180deg) !important;
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pre code, pre code * {
filter: none !important;
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filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
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}
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GitHub - Lisp-Stat/statistics: Statistical functions in Common Lisp · GitHub
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Lisp-Stat Statistics

A consolidation of Common Lisp statistics libraries

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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
About
Statistical functions in Common Lisp
lisp-stat.github.io/statistics
Topics
common-lispstatistics
Resources
Readme
MS-PL license
Contributing
Contributing
Activity
Custom properties
Stars
5 stars
Watchers
3 watching
Forks
4 forks
Report repository
Contributors
Languages