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# simDAG <img src="man/figures/logo.png" height="240" align="right" />

Author: Robin Denz

## Description

`simDAG` is an R-Package which can be used to generate data from a known
directed acyclic graph (DAG) with associated information on
distributions and causal coefficients. The root nodes are sampled first
and each subsequent child node is generated according to a regression
model (linear, logistic, multinomial, cox, …) or other function. The
result is a dataset that has the same causal structure as the specified
DAG and by expectation the same distributions and coefficients as
initially specified. It also implements a comprehensive framework for
conducting discrete-time and discrete-event simulations in a similar
fashion and directly supports network dependencies among individuals.

## Installation

A stable version of this package can be installed from CRAN:

``` r
install.packages("simDAG")
```

and the developmental version may be installed from github using the
`remotes` R-Package:

``` r
library(remotes)

remotes::install_github("RobinDenz1/simDAG")
```

## Bug Reports and Feature Requests

If you encounter any bugs or have any specific feature requests, please
file an [Issue](https://github.com/RobinDenz1/simDAG/issues).

## Examples

Suppose we want to generate data with the following causal structure:

<p align="center">
<img src="man/figures/example_DAG.png" width="450" />
</p>

where `age` is normally distributed with a mean of 50 and a standard
deviation of 4 and `sex` is bernoulli distributed with `p = 0.5` (equal
number of men and women). Both of these “root nodes” (meaning they have
no parents - no arrows pointing into them) have a direct causal effect
on the `bmi`. The causal coefficients are 1.1 and 0.4 respectively, with
an intercept of 12 and a sigma standard deviation of 2. `death` is
modeled as a bernoulli variable, which is caused by both `age` and `bmi`
with causal coefficients of 0.1 and 0.3 respectively. As intercept we
use -15.

The following code can be used to generate 10000 samples from these
specifications:

``` r
library(simDAG)

dag <- empty_dag() +
  node("age", type="rnorm", mean=50, sd=4) +
  node("sex", type="rbernoulli", p=0.5) +
  node("bmi", type="gaussian", formula= ~ 12 + age*1.1 + sex*0.4, error=2) +
  node("death", type="binomial", formula= ~ -15 + age*0.1 + bmi*0.3)

set.seed(42)

sim_dat <- sim_from_dag(dag, n_sim=100000)
```

By fitting appropriate regression models, we can check if the data
really does approximately conform to our specifications. First, lets
look at the `bmi`:

``` r
mod_bmi <- glm(bmi ~ age + sex, data=sim_dat, family="gaussian")
summary(mod_bmi)
#> 
#> Call:
#> glm(formula = bmi ~ age + sex, family = "gaussian", data = sim_dat)
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) 11.89194    0.07954  149.51   <2e-16 ***
#> age          1.10220    0.00158  697.41   <2e-16 ***
#> sexTRUE      0.40447    0.01268   31.89   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for gaussian family taken to be 4.022026)
#> 
#>     Null deviance: 2361465  on 99999  degrees of freedom
#> Residual deviance:  402190  on 99997  degrees of freedom
#> AIC: 422971
#> 
#> Number of Fisher Scoring iterations: 2
```

This seems about right. Now we look at `death`:

``` r
mod_death <- glm(death ~ age + bmi, data=sim_dat, family="binomial")
summary(mod_death)
#> 
#> Call:
#> glm(formula = death ~ age + bmi, family = "binomial", data = sim_dat)
#> 
#> Coefficients:
#>             Estimate Std. Error z value Pr(>|z|)    
#> (Intercept) -14.6833     3.5538  -4.132  3.6e-05 ***
#> age           0.2607     0.1698   1.535    0.125    
#> bmi           0.1842     0.1402   1.314    0.189    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 258.65  on 99999  degrees of freedom
#> Residual deviance: 214.03  on 99997  degrees of freedom
#> AIC: 220.03
#> 
#> Number of Fisher Scoring iterations: 13
```

The estimated coefficients are also very close to the ones we specified.
More examples can be found in the documentation and the multiple
vignettes.

## Citation

If you use this package, please cite the associated article:

Denz, Robin and Nina Timmesfeld (2026). “Simulating Complex
Cross-Sectional and Longitudinal Data using the simDAG R Package”.
Journal of Statistical Software 116 (2), doi: 10.18637/jss.v116.i02.

## License

© 2026 Robin Denz

The contents of this repository are distributed under the GNU General
Public License. You can find the full text of this License in this
github repository. Alternatively, see <http://www.gnu.org/licenses/>.
