num_data_latent

A dataset similar to <code>num_data</code> but with the variable <code>Z</code> treated as a latent variable and thus omitted.

datasets

Tools for causal structure learning from observational data, with emphasis on temporally ordered variables.
The package implements the Temporal Peter–Clark (TPC) algorithm (Petersen, Osler & Ekstrøm, 2021;
<doi:10.1093/aje/kwab087>), the Temporal Greedy Equivalence Search (TGES) algorithm (Larsen, Ekstrøm & Petersen,
2025; <doi:10.48550/arXiv.2502.06232>) and Temporal Fast Causal Inference (TFCI).
It provides a unified framework for specifying background knowledge,
which can be incorporated into the implemented algorithms from the R packages 'bnlearn' (Scutari, 2010;
<doi:10.18637/jss.v035.i03>) and
'pcalg' (Kalish et al., 2012; <doi:10.18637/jss.v047.i11>), as well as the Java library 'Tetrad'
(Scheines et al., 1998; <doi:10.1207/s15327906mbr3301_3>).
The package further includes utilities for visualization, comparison, and evaluation of graph structures,
facilitating performance evaluation and methodological studies.

Bjarke Hautop Kristensen

causalDisco

Tools for Causal Discovery on Observational Data

Frederik Fabricius-Bjerre

Anne Helby Petersen

Claus Thorn Ekstrøm

Tobias Ellegaard Larsen

num_data_latent function

A data.frame with 1000 rows and 4 variables.<dl>
<dt>X1</dt>
<dd>Structural equation: \(X_1 := \sqrt{Z} + \epsilon_1\) with \(\epsilon_1 \sim \mathrm{Unif}[0, 2]\)</dd><dt>X2</dt>
<dd>Structural equation: \(X_2 := 2 \cdot X_3 - \epsilon_2\) with \(\epsilon_2 \sim N(5, 1)\)</dd><dt>X3</dt>
<dd>Structural equation: \(X_3 := \epsilon_3\) with \(\epsilon_3 \sim \mathrm{Unif}[5, 10]\)</dd><dt>Z</dt>
<dd>Structural equation: \(Z := |\epsilon_4|\) with \(\epsilon_4 \sim N(10, 1)\)</dd><dt>Y</dt>
<dd>Structural equation: \(Y := X_1^2 + X_2 - X_3 - Z + \epsilon_5\) with \(\epsilon_5 \sim N(10, 1)\)</dd>
</dl>

Format

Simulated Numerical Data with Latent Variable — num_data_latent

A data.frame with 1000 rows and 4 variables.<dl>
<dt>X1</dt>
<dd>Structural equation: \(X_1 := \sqrt{Z} + \epsilon_1\) with \(\epsilon_1 \sim \mathrm{Unif}[0, 2]\)</dd>

<dt>X2</dt>
<dd>Structural equation: \(X_2 := 2 \cdot X_3 - \epsilon_2\) with \(\epsilon_2 \sim N(5, 1)\)</dd>

<dt>X3</dt>
<dd>Structural equation: \(X_3 := \epsilon_3\) with \(\epsilon_3 \sim \mathrm{Unif}[5, 10]\)</dd>

<dt>Z</dt>
<dd>Structural equation: \(Z := |\epsilon_4|\) with \(\epsilon_4 \sim N(10, 1)\)</dd>

<dt>Y</dt>
<dd>Structural equation: \(Y := X_1^2 + X_2 - X_3 - Z + \epsilon_5\) with \(\epsilon_5 \sim N(10, 1)\)</dd>


</dl>

num_data_latent: Simulated Numerical Data with Latent Variable

Description

Usage

Arguments

Format

Details

See Also

Examples