Description: Functions to draw, manipulate, evaluate directed
acyclic graphs and simulate corresponding data.

Title: R Functions for Directed Acyclic Graphs
Maintainer: Lutz P Breitling <l.breitling@posteo.de>

Title: Directed Acyclic Graphs: Analysis and Data Simulation
Description: Draw, manipulate, and evaluate directed
acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777.

Title: Learning directed acyclic graphs (DAGs) through bootstrap
Description: dagbag is a set of methods that learn DAGs via bootstrap aggregating.

Title: Spatial Vector Data for Danmarks Administrative Geografiske
Description: Compressed spatial vector data originally from <https://dawadocs.dataforsyningen.dk/> saved as Simple
Features, SF, objects with data on population, age and gender from Statistics Denmark <https://www.dst.dk/da/>.

Title: Graphical Analysis of Structural Causal Models
Author: Johannes Textor, Benito van der Zander
Description: A port of the web-based software 'DAGitty', available at
<http://dagitty.net>, for analyzing structural causal models
(also known as directed acyclic graphs or DAGs).
This package computes covariate adjustment sets for estimating causal
effects, enumerates instrumental variables, derives testable
implications (d-separation and vanishing tetrads), generates equivalent
models, and includes a simple facility for data simulation.
Imports: V8, jsonlite, boot, MASS, methods, grDevices, stats, utils,
Suggests: knitr, base64enc (>= 0.1-3), lavaan, testthat

Description: A port of the web-based software "DAGitty" for analyzing
structural causal models (also known as directed acyclic graphs or DAGs).
The package computes covariate adjustment sets for estimating causal

Author: Johannes Textor, Benito van der Zander, Ankur Ankan
Suggests: knitr, base64enc (>= 0.1-3), lavaan, testthat, markdown

Suggests: knitr, base64enc (>= 0.1-3), testthat, markdown, rmarkdown,

Title: DAGs with Omitted Objects Displayed (DAGWOOD)
Description: DAGs With Omitted Objects Displayed (DAGWOOD) is a framework to help reveal key hidden assumptions in a causal DAG. This package provides an implementation of the DAGWOOD algorithm. Further description can be found in the preprint on Haber et al (2021) <arXiv:2004.04251>.
Author: Noah Haber [aut, cre] (<https://orcid.org/0000-0002-5672-1769>)