CRAN/E | BCDAG

BCDAG

Bayesian Structure and Causal Learning of Gaussian Directed Graphs

Installation

About

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) .

Key Metrics

Version 1.0.0
R ≥ 2.10
Published 2022-03-15 745 days ago
Needs compilation? no
License MIT
License File
CRAN checks BCDAG results

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Maintainer

Maintainer

Alessandro Mascaro

a.mascaro3@campus.unimib.it

Authors

Federico Castelletti

aut

Alessandro Mascaro

aut / cre

Material

README
NEWS
Reference manual
Package source

Vignettes

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

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

R ≥ 2.10

Imports

graphics
gRbase
grDevices
lattice
methods
mvtnorm
stats
utils

Suggests

rmarkdown
knitr
testthat ≥ 3.0.0