This data set contains a matrix containing information on five binary
variables (coded as 0/1) and the corresonding DAG model.

datasets

Functions for causal structure
learning and causal inference using graphical models. The main algorithms
for causal structure learning are PC (for observational data without hidden
variables), FCI and RFCI (for observational data with hidden variables),
and GIES (for a mix of data from observational studies
(i.e. observational data) and data from experiments
involving interventions (i.e. interventional data) without hidden
variables). For causal inference the IDA algorithm, the Generalized
Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC)
and some related functions are implemented. Functions for incorporating
background knowledge are provided.

Markus Kalisch

pcalg

Methods for Graphical Models and Causal Inference

Alain Hauser

Martin Maechler

Diego Colombo

Doris Entner

Patrik Hoyer

Antti Hyttinen

Jonas Peters

Nicoletta Andri

Emilija Perkovic

Preetam Nandy

Philipp Ruetimann

Daniel Stekhoven

Manuel Schuerch

Marco Eigenmann

Leonard Henckel

Joris Mooij

gmB function

The format is a list of two components<dl>
 <dt>x:</dt>
<dd>Int [1:5000, 1:5] 0 1 1 0 0 1 1 0 1 1 ...</dd> <dt>g:</dt>
<dd>Formal class 'graphNEL' [package "graph"] with 6 slots 
 .. ..@ nodes : chr [1:5] "1" "2" "3" "4" ... 
 .. ..@ edgeL :List of 5 
 ........</dd> 
</dl>

Format

Graphical Model 5-Dim Binary Example Data — gmB

The format is a list of two components<dl>
 <dt>x:</dt>
<dd>Int [1:5000, 1:5] 0 1 1 0 0 1 1 0 1 1 ...</dd>

 <dt>g:</dt>
<dd>Formal class 'graphNEL' [package "graph"] with 6 slots 
 .. ..@ nodes : chr [1:5] "1" "2" "3" "4" ... 
 .. ..@ edgeL :List of 5 
 ........</dd>

 
</dl>

Graphical Model 5-Dim Binary Example Data

gmB: Graphical Model 5-Dim Binary Example Data

Description

Usage

Arguments

Format

Details

Examples