pyvinecopulib

• pyvinecopulib
  • Introduction
  • Installation
  • Documentation
  • Contributing
• API Documentation
  • Classes
  • Functions
• Examples
  • The bivariate copula (Bicop) API
  • The vine copula (Vinecop) API
  • Fitting a vine copula on dataset and sampling from the model
  • Simulate some data
  • Vine copulas for discrete variables
  • Benchmarking performance
  • The weighted dependence measures (wdm) API
  • The univariate kernel density estimation (Kde1d) API
• Changelog
  • 0.7.6
  • 0.7.5
  • 0.7.4
  • 0.7.3
  • 0.7.1
  • 0.7.0
• Contributing
  • Quick start
  • Repository layout
  • How the build works
  • Makefile cheatsheet
  • Pre-commit hooks
  • CI overview
  • Release process
  • Code style
  • Troubleshooting
  • Tips

What are vine copulas?

Vine copulas are a flexible class of dependence models consisting of bivariate building blocks (see e.g., Aas et al., 2009). You can find a comprehensive list of publications and other materials on vine-copula.org.

What is pyvinecopulib?

pyvinecopulib is the python interface to vinecopulib, a header-only C++ library for vine copula models based on Eigen. It provides high-performance implementations of the core features of the popular VineCopula R library, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over VineCopula are

• a stand-alone C++ library with interfaces to both R and Python,

• a sleaker and more modern API,

• shorter runtimes and lower memory consumption, especially in high dimensions,

• nonparametric and multi-parameter families.

License

pyvinecopulib is provided under an MIT license that can be found in the LICENSE file. By using, distributing, or contributing to this project, you agree to the terms and conditions of this license.

Contact

If you have any questions regarding the library, feel free to open an issue or send a mail to info@vinecopulib.org.

Installation

With pip

The latest release can be installed using pip:

pip install pyvinecopulib

With conda

Similarly, it can be installed with conda:

conda install conda-forge::pyvinecopulib

Or with mamba:

mamba install conda-forge::pyvinecopulib

From source

Start by cloning this repository, noting the --recursive option which is needed for the vinecopulib and wdm submodules:

git clone --recursive https://github.com/vinecopulib/pyvinecopulib.git
cd pyvinecopulib

The main build time prerequisites are:

• scikit-build-core (>=0.5.0),

• nanobind (>=2.7.0),

• libclang (>=18) — used to regenerate src/include/docstr.hpp from the C++ headers as a step of the build,

• numpy / matplotlib / networkx — imported by the post-build stub-generation step,

• a compiler with C++17 support.

When installing via pip install . (the default), all of these are pulled into an isolated build environment automatically via [build-system] requires in pyproject.toml; you don’t need to install them yourself.

To install from source, Eigen and Boost also need to be available, and CMake will try to find suitable versions automatically.

The recommended way to install pyvinecopulib from source is to use conda or mamba. A reproducible environment, also including requirements for the pyvinecopulib’s development and documentation, can be created using:

python scripts/generate_requirements.py --format yml # from pyvinecopulib's root
mamba env create -f environment.yml
mamba activate pyvinecopulib

Alternatively, you can specify manually the location of Eigen and Boost using the environment variables EIGEN3_INCLUDE_DIR and Boost_INCLUDE_DIR respectively. On Linux, you can install the required packages and set the environment variables as follows:

sudo apt-get install libeigen3-dev libboost-all-dev
export Boost_INCLUDE_DIR=/usr/include
export EIGEN3_INCLUDE_DIR=/usr/include/eigen3

Finally, you can build and install pyvinecopulib using pip:

pip install .

The build automatically regenerates src/include/docstr.hpp (from the C++ headers via libclang) and src/pyvinecopulib/__init__.pyi (from the freshly built extension). Both files are gitignored — they’re pure build artifacts.

For an editable install (recommended for development), use --no-build-isolation so the conda env’s libclang is reused and editable.rebuild = true regenerates everything on each import:

pip install -e . --no-build-isolation

Documentation

Stable docs are published at https://pyvinecopulib.readthedocs.io. They are rebuilt automatically by Read the Docs whenever a new release is tagged on main and published to PyPI.

To build the documentation locally:

make docs           # one-shot HTML build → docs/_build/html/
make docs-serve     # live-reload dev server (sphinx-autobuild)

Contributing

Development setup, the build pipeline, the Makefile + pre-commit conventions, the CI workflow, and the release flow are all documented in CONTRIBUTING.md.