Network Analysis and Causal Inference Through Structural Equation Modeling (original) (raw)

Network Analysis and Causal Learning with Structural Equation Modeling

SEMgraph Estimate networks and causal relations in complex systems through Structural Equation Modeling (SEM). SEMgraph comes with the following functionalities:

• Interchangeable model representation as either an igraph object or the corresponding SEM in lavaan syntax. Model management functions include graph-to-SEM conversion, automated covariance matrix regularization, graph conversion to DAG, and tree (arborescence) from correlation matrices.
• Heuristic filtering, node and edge weighting, resampling and parallelization settings for fast fitting in case of very large models.
• Automated data-driven model building and improvement, through causal structure learning and bow-free interaction search and latent variable confounding adjustment.
• Perturbed paths finding, community searching and sample scoring, together with graph plotting utilities, tracing model architecture modifications and perturbation (i.e., activation or repression) routes.

Installation

The latest stable version can be installed from CRAN:

The latest development version can be installed from GitHub:

# install.packages("devtools")
devtools::install_github("fernandoPalluzzi/SEMgraph")

Do not forget to install the SEMdata package too! It contains useful high-throughput sequencing data, reference networks, and pathways for SEMgraph training: