rscopulas 0.2.4

Copula modeling for Python (NumPy) and Rust: vines, pair copulas, and reference-tested pseudo-observation workflows

rscopulas

rscopulas is a Python package for fitting, evaluating, and sampling copula models on validated pseudo-observations, backed by a Rust core built with PyO3 and maturin.

Install

With uv, in a uv project (recommended):

uv add rscopulas

Optional plotting helpers (viz extra):

uv add "rscopulas[viz]"

If you are not using a uv project, install the same packages as with pip:

uv pip install rscopulas
uv pip install "rscopulas[viz]"

Included models

• Gaussian and Student t copulas
• Archimedean families including Clayton, Frank, and Gumbel
• Pair copulas, including Khoudraji constructions
• C-vine, D-vine, and R-vine copulas
• Hierarchical Archimedean copulas

Python example

import numpy as np
from rscopulas import GaussianCopula

data = np.array(
    [
        [0.12, 0.18],
        [0.21, 0.25],
        [0.82, 0.79],
    ],
    dtype=np.float64,
)

fit = GaussianCopula.fit(data)
print("AIC:", fit.diagnostics.aic)
print("sample:\n", fit.model.sample(4, seed=7))

Sampling example

from rscopulas import GumbelCopula

model = GumbelCopula.from_params(2, 2.1)
samples = model.sample(5, seed=11)

print("family:", model.family)
print("samples:\n", samples)
print("log_pdf:\n", model.log_pdf(samples))

Advanced example: fit an R-vine

import numpy as np
from rscopulas import VineCopula

data = np.array(
    [
        [0.10, 0.14, 0.18, 0.22],
        [0.18, 0.21, 0.24, 0.27],
        [0.24, 0.29, 0.33, 0.31],
        [0.33, 0.30, 0.38, 0.41],
        [0.47, 0.45, 0.43, 0.49],
        [0.52, 0.58, 0.55, 0.53],
        [0.69, 0.63, 0.67, 0.71],
        [0.81, 0.78, 0.74, 0.76],
    ],
    dtype=np.float64,
)

fit = VineCopula.fit_r(
    data,
    family_set=["independence", "gaussian", "clayton", "frank", "gumbel"],
    truncation_level=2,
    max_iter=200,
)

print("structure kind:", fit.model.structure_kind)
print("order:", fit.model.order)
print("first-tree families:", [edge.family for edge in fit.model.trees[0].edges])
print("sample:\n", fit.model.sample(4, seed=13))

Project links

• Source: https://github.com/alexwox/rscopulas
• Documentation: https://github.com/alexwox/rscopulas/tree/master/docs
• Issue tracker: https://github.com/alexwox/rscopulas/issues