semvec/pypi-packages huggingface dataset:
etymz 1.1
etymz
License: MIT
Keywords: SOME, MEANINGFULL, KEYWORDS

# Python library quick start
This is a minimal template for uploading your python packages to pypi.org.

## To Use
* First clone this project.
```bash
# Clone this repository
git clone https://github.com/defartsa23/python-library-quick-start.git
# Go into the repository
cd python-library-quick-start
```
* Rename the folder `your-package` to the package name you want. then place all your files into a folder. Open the `__init__.py` file with a text editor of your choice. In this file, you write nothing but the import statement which has the following schema:
```python
from .Filename import Classname
```
* Replace the information in the `setup.py` file with the matching content.
* Upload your package to PyPi
```sh
python setup.py sdist
twine upload dist/*
```
causalImpactReturnSummary 0.0.0
Python Package for causal inference using Bayesian structural time-series models with data returned..
License: MIT
Requires Python: >=2.6

## CausalImpact

[![Python package](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml/badge.svg)](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml)
[![codecov](https://codecov.io/gh/jamalsenouci/causalimpact/branch/master/graph/badge.svg?token=EIPC36VQHS)](https://codecov.io/gh/jamalsenouci/causalimpact)
![monthly downloads](https://pepy.tech/badge/causalimpact/month)
[![DeepSource](https://deepsource.io/gh/jamalsenouci/causalimpact.svg/?label=active+issues&show_trend=true&token=R5aIDSkIId_5THWTAPKccjcH)](https://deepsource.io/gh/jamalsenouci/causalimpact/?ref=repository-badge)

#### A Python package for causal inference using Bayesian structural time-series models

This is a port of the R package CausalImpact, see: https://github.com/google/CausalImpact.

This package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred.

As with all approaches to causal inference on non-experimental data, valid conclusions require strong assumptions. The CausalImpact package, in particular, assumes that the outcome time series can be explained in terms of a set of control time series that were themselves not affected by the intervention. Furthermore, the relation between treated series and control series is assumed to be stable during the post-intervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions.

#### Try it out in the browser

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jamalsenouci/causalimpact/HEAD?labpath=GettingStarted.ipynb)

#### Installation

install the latest release via pip

```bash
pip install causalimpact
```

#### Getting started

[Documentation and examples](https://nbviewer.org/github/jamalsenouci/causalimpact/blob/master/GettingStarted.ipynb)

#### Further resources

- Manuscript: [Brodersen et al., Annals of Applied Statistics (2015)](http://research.google.com/pubs/pub41854.html)

#### Bugs

The issue tracker is at https://github.com/jamalsenouci/causalimpact/issues. Please report any bugs that you find. Or, even better, fork the repository on GitHub and create a pull request.