# MossSpider

MossSpider provides an implementation of the targeted maximum likelihood estimator for network-dependent data
(network-TMLE) in Python. Currently `mossspider`

supports estimation of the conditional network mean for stochastic
policies.

`mossspider`

get its name from the spruce-fir moss spider, a
tarantula that is both the world's smallest tarantula and native to North Carolina.

## Installation

### Installing:

You can install via `python -m pip install mossspider`

### Dependencies:

The dependencies are: `numpy`

, `scipy`

, `statsmodels`

, `networkx`

, `matplotlib`

. Notice that NetworkX must be at least
2.0.0 to work properly.

## Getting started

To demonstrate `mossspider`

, below is a simple demonstration of calculating the mean for the following data.

```
from mossspider import NetworkTMLE
from mossspider.dgm import uniform_network, generate_observed
```

First, we will use some built-in data generating functions

```
graph = uniform_network(n=500, degree=[1, 4])
graph_observed = generate_observed(graph)
```

Now, we can use `NetworkTMLE`

to estimate the causal conditional mean under a stochastic policy. Here, the stochastic
policy sets everyone's probability of action `A=1`

to 0.65.

```
ntmle = NetworkTMLE(network=graph_observed,
exposure='A', # Exposure in graph
outcome='Y', # Outcome in graph
verbose=True) # Print model summaries
ntmle.exposure_model(model="W + W_sum")
ntmle.exposure_map_model(model='A + W + W_sum', # Parametric model
measure='sum', # Summary measure for A^s
distribution='poisson') # Model distribution to use
ntmle.outcome_model(model='A + A_sum + W + W_sum')
ntmle.fit(p=0.65, samples=500)
ntmle.summary()
```

For full details on using `mossspider`

, see the full documentation and worked examples available
at MossSpider website.