semvec/pypi-packages huggingface dataset:
grmpy 0.1.10
grmpy is a Python package for the simulation and estimation of the generalized Roy model.
License: MIT

grmpy is a Python package for the simulation and estimation of the generalized Roy model.

# grmpy

``grmpy``  is an open-source Python package for the simulation and estimation of the generalized Roy model. It serves as a teaching tool to promote the conceptual framework of the generalized Roy model, illustrate a variety of issues in the econometrics of policy evaluation, and showcases basic software engineering practices. <br>
Marginal Treatment Effects (MTE) can be estimated based on a parametric normal model or,
alternatively, via the semiparametric method of Local Instrumental Variables (LIV).

You can install ```grmpy``` either via pip

```
$ pip install grmpy
```
Or download it directly from our GitHub repository and install the package in editable mode

```
$ git clone https://github.com/OpenSourceEconomics/grmpy.git
$ pip install -e .
```

---
## Quick Start
> Initialization File

```grmpy``` relies on an ```"initialization.yml"``` file (referred to as ``ìnit_file`` below)
to perform both simulation and estimation.
For example, check out these two ``init_files`` for
[simulation and parametric estimation](https://github.com/OpenSourceEconomics/grmpy/blob/master/promotion/grmpy_tutorial_notebook/files/tutorial.grmpy.yml) as well as 
a [semiparametric estimation](https://github.com/OpenSourceEconomics/grmpy/blob/master/promotion/grmpy_tutorial_notebook/files/tutorial_semipar.yml) setup.

Below you'll find some example code you can copy to jump-start your project.  

> Simulation
```
import grmpy

# Specify the initilaization file you want to use, e.g.:
init_file = "ProjectFiles/simulation.yml"

data = grmpy.simulate(init_file)
```
> Estimation

```
import grmpy

# Specify the initilaization file you want to use, e.g.:
init_file = "ProjectFiles/estimation.yml"

# Parametric Normal Model
rslt = grmpy.fit(init_file, semipar=False)
grmpy.plot_mte(rslt, init_file, color="blue", semipar=False, save_plot="MTE_par.png")

# Local Instrumental Variables (Semiparametric Model)
rslt = grmpy.fit(init_file, semipar=True)
grmpy.plot_mte(rslt, init_file, color="orange", semipar=True, nboot= 250, save_plot="MTE_semipar.png")
```

Please visit our [online documentation](http://grmpy.readthedocs.io/) for tutorials and more.

-----
[![docs passing](https://travis-ci.org/OpenSourceEconomics/grmpy.svg?branch=master)]()
[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)]()
[![code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)
pydocgen 0.0.4rc0
Automatically generate docstrings and documentation files for your code!
License: MIT

PyDocGen
--------
Automatically generate docstrings and documentation for your Python modules!

Installation
--------------
```
pip install pydocgen
```

Usage
-------
Pydocgen currently will accept a Python module file, it will support packages in the near future
```
pydocgen --help         #View all options
pydocgen -i file_name
```

Features
----------
- Easy to use, quick way to document your code
- Input module file will automatically be decorated with docstrings (under development)
    - Only need to fill in variable and function description
    - Automatic type inference!
- Documentation files will be generated automatically (current supports HTML)
ua-eds-api 1.0.1
Provides easy interface for making REST requests to University of Arizona EDS registry.
License: MIT

# UA-EDS-API
Provides easy interface for making REST requests to University of Arizona EDS registry.

## Motivation
To make a python API that could generically interact with the REST architecture of EDS.

## Code Example
```python
from ua_eds_api import ua_eds_api

eds_api = ua_eds_api.EdsApi("host", "username", "password", "grouper url")

users = eds_api.get_grouper_users("grouper endpoint")
```

## Installation
pip install --user ua-eds-api

## Credits
[RyanJohannesBland](https://github.com/RyanJohannesBland)
[EtienneThompson](https://github.com/EtienneThompson)

## License
MIT
auditless-py-solc-x 0.1.0
py-solc-x wrapper for Auditless
License: MIT
Requires Python: >=3.8,<4.0

# py-solc-x wrapper for Auditless

This wrapper modifies `py-solcx-x` to produce output on every compilation that can be consumed by Auditless.

## Can I use this for my project

As long as you are using `py-solc-x` to compile Solidity files, you can use this wrapper.

## How to use

Add the the following snippet to your code:

```python
from pathlib import Path
from auditless_solcx import solcx_start_saving_debugging_output_to_path

path = Path(__file__).parent  # This will save files in a folder ./artifacts/build-info

solcx_start_saving_debugging_output_to_path(path)
# This needs to appear before any modules consuming `py-solc-x` are loaded
# See below "Important note about patching"
```

## Important note about patching

The patching function `solcx_start_saving_debugging_output_to_path(<path>)` needs to be called either before
`solcx` is used directly or before any module that imports and uses `solcx`.

See [Python Mock Gotchas](https://alexmarandon.com/articles/python_mock_gotchas/) for more information about patching
order.

## License

MIT