↙️ ↘️ An R package for working with causal directed acyclic graphs (DAGs)

R package for building flexible workflows

causalMGM is an R package that allow users to learn undirected and directed (causal) graphs over mixed data types (i.e., continuous and discrete variables).

The simMixedDAG package enables simulation of "real life" datasets from DAGs

The orientDAG package is used to orient DAG edges. It also includes utility functions to convert DAGs between different representations as well as measure DAG dissimilarity measures.

More than DAGs in R... An R package to visualise, log, and monitor DAG execution/progression using special comments and drop in functions

The simMixedDAG package enables simulation of "real life" datasets from DAGs

An R package for working with directed acyclic graphs (DAGs)

Directed Graphical Models and Causal Discovery for Zero-Inflated Data.

Bayesian graphical models estimated with projection predictive selection

R code for causal graph animations

Network-based clustering

Statistical comparison of two networks with respect to global strength

Automatic network analysis of psychological data

Simulation code for the paper "Canonical causal diagrams to guide the treatment of missing data in epidemiological studies"

Bayesian Gaussian Graphical Models

List of papers with DAGs.

Easy DAG-based robustness checks for R: classifies variables by causal role, computes the smallest and largest permissible back-door adjustment sets, and compare the significance of models.