TLLiNGAM: Learning linear non-Gaussian DAG via topological layers.

In TransGraph: Transfer Graph Learning

Learning linear non-Gaussian DAG via topological layers.

Description

Learning linear non-Gaussian DAG via topological layers.

Usage

TLLiNGAM (X, hardth=0.3, criti.val=0.01, precision.refit = TRUE,
                 precision.method="glasso", B.refit=TRUE)

Arguments

X	The n * p sample matrix, where n is the sample size and p is data dimension.
hardth	The hard threshold of regression.
criti.val	The critical value of independence test based on distance covariance.
precision.refit	Whether to perform regression for re-fitting the coefficients in the precision matrix to improve estimation accuracy, after determining the non-zero elements of the precision matrix. The default is True.
precision.method	Methods for Estimating Precision Matrix, which can be selected from "glasso" and "CLIME".
B.refit	Whether to perform regression for re-fitting the coefficients in structural equation models to improve estimation accuracy, after determining the parent sets of all nodes. The default is True.

Value

A result list including:

A

The information of layer.

B

The coefficients in structural equation models.

Author(s)

Ruixuan Zhao [e-mail], Xin He, and Junhui Wang

References

Zhao, R., He X., and Wang J. (2022). Learning linear non-Gaussian directed acyclic graph with diverging number of nodes. Journal of Machine Learning Research.

TransGraph documentation built on Nov. 12, 2025, 9:06 a.m.