Metadata-Version: 2.1
Name: Shelegia-Motta-2021
Version: 0.0.3
Summary: Implements the model presented in Shelegia and Motta (2021)
Home-page: https://github.com/manuelbieri/shelegia_motta_2021
Author: Manuel Bieri
Author-email: manuel.bieri@outlook.com
License: MIT
Download-URL: https://github.com/manuelbieri/Shelegia_Motta_2021/archive/refs/tags/v0.0.2.tar.gz
Keywords: Acquisition,Kill Zone,Start-up
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib (>=3.4.3)
Requires-Dist: numpy (>=1.17)

This package implements the models of [Shelegia and Motta (2021)](shelegia_motta_2021.pdf).

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### Installation
Installation over [PyPI](https://pypi.org/project/Shelegia-Motta-2021/):
```
pip install Shelegia-Motta-2021
```

Or clone the repository via [GitHub](https://github.com/manuelbieri/shelegia_motta_2021):
```
git clone manuelbieri/shelegia_motta_2021
```

### Introduction
Since all models implement the Shelegia_Motta_2021.IModel.IModel - Interface, therefore all models provide the same functionality (public methods), even though the results may change substantially.

For all models add the following import statement:
```
import Shelegia_Motta_2021.Models
```

### Models
#### Base Model
```
base_model = Shelegia_Motta_2021.Models.BaseModel()
```

#### Unobservable Choices Model
```
unobservable_model = Shelegia_Motta_2021.Models.UnobservableModel()
```

#### Acquisition Model
```
acquisition_model = Shelegia_Motta_2021.Models.AcquisitionModel()
```

#### Two-sided Market Model
```
two_sided_market_model = Shelegia_Motta_2021.Models.TwoSidedMarketModel()
```

### Basic usage
```
# every model type can be plugged in
model: Shelegia_Motta_2021.IModel.IModel = Shelegia_Motta_2021.Models.BaseModel()

# print string representation of the model
print(model)

# plot the best answers of the incumbent to the choice of the entrant
model.plot_incumbent_best_answers()

# plot the equilibrium path
model.plot_equilibrium()
```

### Documentation
For the latest version of the documentation open [manuelbieri.github.io/shelegia_motta_2021](https://manuelbieri.github.io/shelegia_motta_2021/Shelegia_Motta_2021.html) in your browser or call:
```
import Shelegia_Motta_2021

Shelegia_Motta_2021.docs()
```

#### Build Documentation
Install the pdoc package:
```
pip install pdoc
```
Generate api-documentation with the following command:
```
pdoc -o ./docs Shelegia_Motta_2021 --docformat "numpy" --math
```

