Network Analysis and Causal Inference Through Structural Equation Modeling
Amyotrophic Lateral Sclerosis (ALS) dataset
Node ancestry utilities
Topological graph clustering
Module scoring
Vertex and edge graph coloring on the base of fitting
Subgraph mapping
Graph conversion from dagitty to igraph
Cluster extraction utility
Factor analysis for high dimensional data
Graph plotting with renderGraph
Convert directed graphs to directed acyclic graphs (DAGs)
Graph conversion from igraph to dagitty
Graph to lavaan model
lavaan model to graph
Conditional Independence (CI) local tests of an acyclic graph
Graph nodes merging by a membership attribute
Optimal model search strategies
Assign edge orientation of an undirected graph
Pairwise plotting of multivariate data
Parameter Estimates of a fitted SEM
Perturbed path search utility
Graph properties summary and graph decomposition
Interactome-assisted graph re-seizing
Compute the Average Causal Effect (ACE) for a given source-sink pair
Bow-free covariance search and data de-correlation
Estimate a DAG from an input (or empty) graph
SEM-based differential network analysis
SEM-based gene set analysis
Search for directed or shortest paths between pairs of source-sink nod...
Fit a graph as a Structural Equation Model (SEM)
Tree-based structure learning methods
Missing edge testing implied by a DAG with Shipley's basis-set
GGM model summary
RICF model summary
Transform data methods
Graph weighting methods
Estimate networks and causal relationships in complex systems through Structural Equation Modeling. This package also includes functions for importing, weight, manipulate, and fit biological network models within the Structural Equation Modeling framework as outlined in the Supplementary Material of Grassi M, Palluzzi F, Tarantino B (2022) <doi:10.1093/bioinformatics/btac567>.