BCDAG: Bayesian Structure and Causal Learning of Gaussian Directed Graphs

A collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables. References: F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>, F. Castelletti and A. Mascaro (2022) <doi:10.48550/arXiv.2201.12003>.

README.md Elaborate on the output of `learn_DAG()` using get_ functions MCMC scheme for posterior inference of Gaussian DAG models: the `learn_DAG()` function Random data generation from Gaussian DAG models

Vignettes Man pages API and functions Files

Package details
Author	Federico Castelletti [aut], Alessandro Mascaro [aut, cre, cph]
Maintainer	Alessandro Mascaro <alessandro.mascaro@upf.edu>
License	MIT + file LICENSE
Version	1.1.3
URL	https://github.com/alesmascaro/BCDAG
Package repository	View on CRAN
Installation	Install the latest version of this package by entering the following in R: `install.packages("BCDAG")`