SmithWagnerCV

Name	SmithWagnerCV JSON
Version	0.1.0 JSON
	download
home_page
Summary	Produces critical values for value-added learning scores proposed in Smith and Wagner (2018) through Monte Carlo simulations.
upload_time	2023-06-23 19:04:27
maintainer
docs_url	None
author
requires_python	>=3.8
license
keywords	monte carlo statistics value-added learning
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
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            # SmithWagnerCV
 
This module produces critical values for the disaggregated learning types as described in Smith and Wagner (2018) and Smith and White (2021). 

## Examples

Run a Monte Carlo Simulation of mu value of 0.1 and 25 students.

```python
from SmithWagnerCV import RunSimulation

d = RunSimulation(25, 0.1)

```

Simulate all combinations of [10,20] students and [0.1,0.5] mu values and return them as a dictionary

```python
from SmithWagnerCV import SimulationTable

d = SimulationTable([10,20], [0.1,0.5])

```
Simulate all combinations of [10,20] students and [0.1,0.5] mu values and save them to CSV files

```python
from SmithWagnerCV import SaveSimulationTable 

d = SaveSimulationTable([10,20], [0.1,0.5])

```

## Installation

Using the pip tool, you can install this module with the following command:

```
pip install SmithWagnerCV
```

Using the conda command you can type the following:

```
conda install -c tazzben smithwagnercv  
```

            

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