pcglasso 0.1.0

Partial Correlation Graphical LASSO (PCGLASSO) — fast Rust-backed Python implementation

pcglasso

pcglasso is a Python package for finding direct relationships between variables in noisy, high-dimensional data.

Instead of asking which variables are merely correlated, PCGLASSO estimates which variables remain connected after accounting for all the others. The result can be read as a sparse network: variables are nodes, and selected edges are direct conditional relationships.

The package exposes a small Python API and runs the iterative solver in Rust for speed.

What you can use it for

• Build an interpretable network from tabular data.
• Separate direct relationships from indirect correlations.
• Estimate sparse Gaussian graphical models.
• Work with data where variable scales differ, because PCGLASSO is scale invariant.
• Fit repeated, similar problems efficiently with warm starts.

Common use cases include genomics, neuroscience, finance, survey analysis, and other settings where many variables may be related but only some relationships are direct.

Install

pip install pcglasso

To build from source, use maturin with Python 3.10 or newer:

pip install maturin
maturin develop --release

Quick start

Use PCGLasso like a small sklearn-style estimator:

import numpy as np
from pcglasso import PCGLasso

X = np.random.default_rng(0).standard_normal((200, 20))

model = PCGLasso(alpha=0.1).fit(X)

model.partial_correlation_  # strength of direct relationships
model.adjacency_            # selected network edges
model.precision_            # estimated inverse covariance matrix

The most readable output is often adjacency_, a boolean matrix showing which variables are connected after the model has removed weaker indirect effects.

If you already have a covariance or correlation matrix, use the functional API:

from pcglasso import pcglasso

res = pcglasso(S, alpha=0.1, c=None, method='dual')
res.precision_, res.partial_correlation_, res.objective_

Choosing alpha

alpha controls how sparse the network is:

• Larger alpha values remove more edges and produce simpler networks.
• Smaller alpha values keep more edges and produce denser networks.

There is no universal best value. In practice, choose alpha by validation, stability analysis, domain knowledge, or by fitting a sequence of values and inspecting how the graph changes.

Warm starts

Set warm_start=True to reuse the previous solution as the next fit's starting point. This is useful for a sequence of similar problems, such as bootstrap resamples or a path of nearby alpha values.

model = PCGLasso(alpha=0.1, warm_start=True)
graphs = []
for X_b in resamples:
    model.fit(X_b)
    graphs.append(model.precision_.copy())

Main outputs

• partial_correlation_: direct relationship strengths on a common scale.
• adjacency_: boolean conditional-dependence graph with a zero diagonal.
• precision_: estimated precision matrix.
• covariance_: model-implied covariance matrix.
• n_iter_ and converged_: basic solver diagnostics.

Advanced options

PCGLASSO estimates a sparse Gaussian precision matrix by penalising partial correlations rather than raw precision-matrix entries. This is what makes the estimator scale invariant and helps with hub-structured graphs.

The package includes two coordinate-descent solvers from Bogdan et al. (2026):

• method='primal' (default): uses the pcglassoFast approach and returns partial correlations directly. This is a good default, especially for hub-structured problems.
• method='dual': uses the pcglassoFast_Dual approach, adapted from the GLASSO dual. This can be faster on some generic sparse problems.

Both solvers use a Rust core through PyO3 and maturin. The hot loop does not require BLAS or LAPACK, which helps keep wheels portable.

Other parameters:

• c: diagonal parameter. When None, the package chooses a data-dependent default.
• max_iter: maximum number of outer iterations.
• tol: convergence tolerance.
• assume_centered: whether input data has already been centered.

Status

This is an early Python implementation. The implementation follows the original R package and the source papers; CI currently builds the package and runs a smoke test across Linux, macOS, and Windows.

References

• Carter, Rossell & Smith (2024). Partial correlation graphical LASSO. Scandinavian Journal of Statistics.
• Carter & Molinari (2025). Existence and optimisation of the partial correlation graphical lasso.
• Bogdan, Chojecki, Hejný, Kołodziejek & Wallin (2026). Identifying network hubs with the partial correlation graphical LASSO.