A Python library for fitting and sampling vine copulas using PyTorch.
-
Updated
Sep 26, 2025 - Python
#
copula
Here are 91 public repositories matching this topic...
Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python
python correlation entropy information-theory variable-selection causality copula hypothesis-testing mutual-information changepoint transfer-entropy conditional-mutual-information granger-causality change-detection change-point-detection causal-discovery two-sample-test copula-entropy normality-test conditional-independence-test
-
Updated
Oct 2, 2024 - Python
Multivariate data modelling with Copulas in Python
python data statistics modeling dependency-analysis pypi conda python3 data-analysis copula pypi-packages copula-models copulas dependency-modeling copulae
-
Updated
Feb 7, 2025 - Jupyter Notebook
Python library for multivariate dependence modeling with Copulas
python simulation multivariate simulations copula mixture tdc dependence tail-dependence-coefficient
-
Updated
Jun 11, 2024 - Python
📈 🐍 Multidimensional synthetic data generation with Copula and fPCA models in Python
weather finance data-science machine-learning statistics climate dependency-analysis xarray data-generation data-generator copula principal-component-analysis data-augmentation augmentation oversampling synthetic-data fpca data-modelling dependency-modeling functional-data
-
Updated
Sep 28, 2023 - Python
Supplementary material for ICDM 20 paper "COPOD: Copula-Based Outlier Detection"
-
Updated
Aug 31, 2020 - Python
Python package for canonical vine copula trees with mixed continuous and discrete marginals
python statistics modeling dependency-analysis data-analysis copula regular-vines c-vines copula-models copulas dependency-modeling copulae
-
Updated
Dec 21, 2023 - Python
R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
r correlation entropy information-theory variable-selection causality copula mutual-information transfer-entropy conditional-mutual-information granger-causality change-point-detection causal-discovery two-sample-test mutualinf copula-entropy normality-test conditional-independence-test
-
Updated
Jun 7, 2024 - R
A C++ library for vine copula models (w/ interfaces to R + Python)
-
Updated
Nov 22, 2025 - C++
R interface to the vinecopulib C++ library
-
Updated
Oct 20, 2025 - C++
ARMA-GARCH Mixture Copula Mean-CVaR portfolio optimization project.
-
Updated
Jan 28, 2021 - R
A professional, research-grade comparison of Gaussian Copula and Variational Autoencoder (VAE) methods for synthetic tabular data generation. Includes full evaluation pipeline with distribution overlap, correlation analysis, PCA projections, pairplots, metrics, and automated visual reports.
python data-science machine-learning deep-learning simulation scikit-learn tabular-data data-visualization pytorch generative-model pca vae research-project data-privacy copula data-augmentation synthetic-data probabilistic-model statistical-modeling correlation-analysis
-
Updated
Nov 11, 2025 - Python
Matlab toolbox for canonical vine copula trees with mixed continuous and discrete marginals
statistics matlab modeling dependency-analysis data-analysis copula regular-vines c-vines copula-models copulas dependency-modeling copulae
-
Updated
Jul 15, 2017 - MATLAB
A narrative and technical exploration of data authenticity through the four pillars of synthetic data realism, Fidelity, Coverage, Privacy, and Utility. This thought-leadership piece combines storytelling, mathematics, and code to explain how these metrics define the ethical and functional “soul” of data in AI systems.
python data-science benchmark coverage machine-learning utility privacy evaluation gan vae copula diffusion data-quality fidelity synthetic-data explainable-ai ai-ethics responsible-ai generative-ai autocurator
-
Updated
Nov 14, 2025
Autocurator is a comprehensive benchmarking toolkit for evaluating synthetic tabular data. It measures fidelity, coverage, privacy, and utility through quantitative metrics, visual reports, and PCA/correlation diagnostics. Ideal for validating VAE, GAN, Copula, or Diffusion-generated datasets.
python data-science benchmark coverage machine-learning utility privacy deep-learning metrics data-validation evaluation pytorch gan vae data-privacy copula diffusion data-quality fidelity synthetic-data
-
Updated
Nov 11, 2025 - Python
Tools to construct canonical and regular vines. StarVine can also be used as a bivariate copula fitting tool.
-
Updated
Oct 19, 2020 - Python
[Quantitative Finance 2019] Sovereign Risk Zones in Europe During and After the Debt Crisis
-
Updated
May 12, 2020 - Python
Improve this page
Add a description, image, and links to the copula topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the copula topic, visit your repo's landing page and select "manage topics."