Python copulae library for dependency modelling

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for DannieBee from gravatar.com DannieBee

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Daniel Bok
  • Maintainer: Daniel Bok
  • Tags copula, copulae, dependency modelling, dependence structures, archimdean, elliptical, finance
  • Requires: Python >=3.6
Classifiers

Project description

Copulae

Probably the second most popular copula package in Python. 😣

Copulae is a package used to model complex dependency structures. Copulae implements common and popular copula structures to bind multiple univariate streams of data together. All copula implemented are multivariate by default.

Versions

Anaconda Version PyPI version

Continuous Integration

Build Status Anaconda-Server Badge Downloads Anaconda-Server Badge

Documentation

Documentation Status

Coverage

Coverage Status

Installing

Install and update using pip and on conda.

# conda
conda install -c conda-forge copulae 

# PyPI
pip install -U copulae

Documentation

The documentation is located at https://copulae.readthedocs.io/en/latest/. Please check it out. :)

Simple Usage

from copulae import NormalCopula
import numpy as np

np.random.seed(8)
data = np.random.normal(size=(300, 8))
cop = NormalCopula(8)
cop.fit(data)

cop.random(10)  # simulate random number

# getting parameters
p = cop.params
# cop.params = ...  # you can override parameters too, even after it's fitted!  

# get a summary of the copula. If it's fitted, fit details will be present too
cop.summary()

# overriding parameters, for Elliptical Copulae, you can override the correlation matrix
cop[:] = np.eye(8)  # in this case, this will be equivalent to an Independent Copula

Most of the copulae work roughly the same way. They share pretty much the same API. The difference lies in the way they are parameterized. Read the docs to learn more about them. 😊

Acknowledgements

Most of the code has been implemented by learning from others. Copulas are not the easiest beasts to understand but here are some items that helped me along the way. I would recommend all the works listed below.

Elements of Copula Modeling with R

I referred quite a lot to the textbook when first learning. The authors give a pretty thorough explanation of copula from ground up. They go from describing when you can use copulas for modeling to the different classes of copulas to how to fit them and more.

Blogpost from Thomas Wiecki

This blogpost gives a very gentle introduction to copulas. Before diving into all the complex math you'd find in textbooks, this is probably the best place to start.

Motivations

I started working on the copulae package because I couldn't find a good existing package that does multivariate copula modeling. Presently, I'm building up the package according to my needs at work. If you feel that you'll need some features, you can drop me a message. I'll see how I can schedule it. 😊

TODOS

  • Set up package for pip and conda installation
  • More documentation on usage and post docs on rtd (Permanently in the works 😊)
  • Elliptical Copulas
    • Gaussian (Normal)
    • Student (T)
  • Implement in Archimedean copulas
    • Clayton
    • Gumbel
    • Frank
    • Empirical
    • Joe
    • AMH
    • Rho finding via Cubatures
  • Mixture copulas
    • Gaussian Mixture Copula
    • Generic Mixture Copula
    • Marginal Copula
  • Vine Copulas
  • Copula Tests
    • Radial Symmetry
    • Exchangeability
    • Goodness of Fit
      • Pairwise Rosenblatt
      • Multi-Independence
      • General GOF
    • Model Selection
      • Cross-Validated AIC/BIC

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for DannieBee from gravatar.com DannieBee

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Daniel Bok
  • Maintainer: Daniel Bok
  • Tags copula, copulae, dependency modelling, dependence structures, archimdean, elliptical, finance
  • Requires: Python >=3.6
Classifiers

Release history Release notifications | RSS feed

0.7.9

0.7.8

This version

0.7.7

0.7.6

0.7.5

0.7.4

0.7.3

0.7.2

0.7.1

0.7.0

0.6.0

0.5.2

0.5.1

0.5.0

0.4.3

0.4.2

0.4.1

0.4.0

0.3.1

0.3.0

0.2.2

0.2.1

0.2.0

0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

copulae-0.7.7.tar.gz (660.6 kB view details)

Uploaded Source

Built Distributions

copulae-0.7.7-cp310-cp310-win_amd64.whl (995.8 kB view details)

Uploaded CPython 3.10 Windows x86-64

copulae-0.7.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

copulae-0.7.7-cp310-cp310-macosx_10_9_x86_64.whl (998.7 kB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

copulae-0.7.7-cp39-cp39-win_amd64.whl (995.6 kB view details)

Uploaded CPython 3.9 Windows x86-64

copulae-0.7.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

copulae-0.7.7-cp39-cp39-macosx_10_9_x86_64.whl (998.7 kB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

copulae-0.7.7-cp38-cp38-win_amd64.whl (994.9 kB view details)

Uploaded CPython 3.8 Windows x86-64

copulae-0.7.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

copulae-0.7.7-cp38-cp38-macosx_10_9_x86_64.whl (990.7 kB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

copulae-0.7.7-cp37-cp37m-win_amd64.whl (990.3 kB view details)

Uploaded CPython 3.7m Windows x86-64

copulae-0.7.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

copulae-0.7.7-cp37-cp37m-macosx_10_9_x86_64.whl (992.9 kB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file copulae-0.7.7.tar.gz.

File metadata

  • Download URL: copulae-0.7.7.tar.gz
  • Upload date:
  • Size: 660.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for copulae-0.7.7.tar.gz
Algorithm Hash digest
SHA256 0f1ff2d0a538b3b7c9270955782caeac289165db589bfc06c889fa4f6590a6be
MD5 78d98c24e31795a5e7e0ea4230fef5e2
BLAKE2b-256 ba04eb3ea9bed2dd87b8969ba211b5fc3e7ece6806ae3fb45b578f61017ca97c

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: copulae-0.7.7-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 995.8 kB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for copulae-0.7.7-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 c3993bffd275ed8420cf74120047c362e2de5c943bb776059e00c7219bf0799b
MD5 fcaa461ac1652a672f8f80e361a6d95e
BLAKE2b-256 f950fa7d741aeda6d6246c13efd8a6b25bb1e309bd22a59c002e693e7625f9cd

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for copulae-0.7.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 afb9db3c7734070a39f6edc9acae96e695e228f0aaad9c930b859b0415cbb355
MD5 6177bc124af3727aded3181612d724c8
BLAKE2b-256 bf6f44092141ea344170b6afa3b660da06d0166430313357f61fd4d707ab70df

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: copulae-0.7.7-cp310-cp310-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 998.7 kB
  • Tags: CPython 3.10, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for copulae-0.7.7-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 636d6d78e58e88d4f715e4ddb6fd03103c01e83e16bfeec906a4e66d092630a4
MD5 23c3157973f10376cc97f79621611bf4
BLAKE2b-256 067b5ebc9641915bce809a7dfad9a592e2448a0e602028d98b143f3148a49abf

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: copulae-0.7.7-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 995.6 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for copulae-0.7.7-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 e4d98d05384ed96116f5d90244f6dde9f31516cb042e4eaf92959bcf0d141aa1
MD5 b0d2b1d1534fc13e348efdb571bd32e3
BLAKE2b-256 934d56e2266d4e3a571e097d9495578006edeaa2ef20d465247abbafab7a683a

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for copulae-0.7.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d78056230584f863ed1ece1f57cc92de4186e9d95fb826f551ad2e702bfae43d
MD5 71f0c84083f387a81145bf93d78750eb
BLAKE2b-256 daa02dba7496aceca1dc867a141df68cdbb1295d94060779feb1b593b5be79aa

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: copulae-0.7.7-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 998.7 kB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for copulae-0.7.7-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 eb073f915e2e226a3ad04210b6116c83e6940e0f95ebdbe8df92b796c17c85a5
MD5 fc9c33afcb90d9a0ee5eb816bbc0efaa
BLAKE2b-256 3ce9aaf25423d028030da616b922d0ded2aebe7450c708d0cfc87a2e1ddf8df2

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: copulae-0.7.7-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 994.9 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for copulae-0.7.7-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 465feaf07cae826b44507f136422d230fd35eebe1d82837705b42c3630b2ef47
MD5 630d615d30efa2d31c54c54d0e3d9387
BLAKE2b-256 b305cc0c37831a80ec3851d346444d9793d66e602220b2f1485756e2a7535846

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for copulae-0.7.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0499ee10b3a2f4293769cd66b31e5e0b35847e7c93172233488ea53d004ac4f7
MD5 17e03074fd50ec191327f4c5b1351778
BLAKE2b-256 fbdc047050fd6ef0fa9756b55f76a495d154fce0b73c87993eb3d5b6c7c4ee70

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: copulae-0.7.7-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 990.7 kB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for copulae-0.7.7-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a1c674049963e504e2885de206f6c07a1d45e63311c7352c87bd18cfcb10e1fc
MD5 4f1df8a9c6371011f24bfb26e980369f
BLAKE2b-256 678afcf5d6954c7605ec8ee50e3ea19b64c0769129bf8d4c966a0827fea0fa53

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: copulae-0.7.7-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 990.3 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for copulae-0.7.7-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 f4aa08f62a23e1c05a18d0db84a63f9f6826eda101765656735f9d2994eca61f
MD5 8a4757798c5a5ea4ed0ac0615d55ff00
BLAKE2b-256 476a4e9cfcf4bcd5ee4839854b3bc51197fb3f70709bd45aa615a6e257f18f59

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for copulae-0.7.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a90b506f5770ec6b50837edb3d6c7d27935c9dae6760e983a3128a426e9a96ac
MD5 74fd46474904bd5c696f9e2f82ac9254
BLAKE2b-256 3427b27fe4e0652109b3445f771458c2a6713cc0234d9c60dd4f895cdbd2e351

See more details on using hashes here.

File details

Details for the file copulae-0.7.7-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: copulae-0.7.7-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 992.9 kB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for copulae-0.7.7-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7f51ab9559106b871acb6229fe7291a5c5be5d260d3a4ef2d4bf3627e3c056fa
MD5 7e7f7d85eebc6c3b2dbf9bd5258959a3
BLAKE2b-256 ddffef9353ce33dcd9dbf3f8826140fdfc97291e6cb4aa0b468c158ba7c7cb2a

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page