Metadata-Version: 2.4
Name: causalspyne
Version: 0.1.6
Summary: hierarchical data generation for causal discovery and abstraction
Requires-Python: >=3.12
Requires-Dist: causal-learn>=0.1.4.3
Requires-Dist: matplotlib>=3.10.7
Requires-Dist: networkx>=3.5
Requires-Dist: numpy>=2.3.4
Requires-Dist: pandas>=2.3.3
Requires-Dist: scipy>=1.16.2
Description-Content-Type: text/markdown

# CausalSpyne
[![PyPI version](https://badge.fury.io/py/causalspyne.svg)](https://badge.fury.io/py/causalspyne)  [![test coverage](https://marrlab.github.io/causalspyne/coverage-badge.svg)](https://marrlab.github.io/causalspyne/)

A Python package for simulating data from confounded causal models.


## Quick start
Install with: `pip install causalspyne`

Generate some data:
```
from causalspyne import gen_partially_observed


gen_partially_observed(size_micro_node_dag=4,
                       num_macro_nodes=4,
                       degree=2,  # average vertex/node degree
                       list_confounder2hide=[0.5, 0.9], # choie of confounder to hide: percentile or index of all toplogically sorted confounders
                       num_sample=200,
                       output_dir="output",
                       rng=0)
```

## Submodules

```
git submodule update --init
```
