Causal network inference and discovery 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 graph creation 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.

The latest stable version can be installed from CRAN:

install.packages("SEMgraph")

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:

devtools::install_github("fernandoPalluzzi/SEMdata")

See our website HERE for help and examples.

Next versions of SEMgraph will include new functionalities for de novo (data-driven) causal model learning and new inference methods.

Palluzzi F, Grassi M. SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models. Aug 2021; arXiv:2103.08332.