Metadata-Version: 2.1
Name: MONarchy
Version: 1.0.7
Summary: MON (Meadian of meaNs)
Home-page: https://github.com/prise-3d/MONarchy
Maintainer: Samuel DELEPOULLE
Maintainer-email: samuel.delepoulle@univ-littoral.fr
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas

# MONarchy module for MON estimators #

![](https://img.shields.io/github/workflow/status/prise-3d/MONarchy/build?style=flat-square) ![](https://img.shields.io/pypi/v/MONarchy?style=flat-square) ![](https://img.shields.io/pypi/dm/MONarchy?style=flat-square)

## MONArchy provides : ##
- estimation functions using MON and derivative methods
- The MONArchy class to call each function on a set of data

## Analyse.py ##
- Analyse : to load data and return a JSON file with estimations and descriptive statistics

exemple : 
```
a = Analyse(path)
print(a.head())

print(a.infos())
a.save_graph("0_0_R","fig.png")
```
with 
- ``path`` : the path of a CSV file (string)
- ``column_name`` : the column name (string)

produce a JSON file with statistical estimators

## Changelog 

### 1.0.7
- correct requirements.txt

### 1.0.6
- add save_graph in Analyse

### 1.0.5 
- add bayesian MoN 

## Refercences 

@article{orenstein_robust_2019,
	title = {Robust Mean Estimation with the Bayesian Median of Means},
	url = {http://arxiv.org/abs/1906.01204},
	journaltitle = {{arXiv}:1906.01204 [math, stat]},
	author = {Orenstein, Paulo},
	urldate = {2021-04-08},
	date = {2019-06-04},
	eprinttype = {arxiv},
	eprint = {1906.01204},
	keywords = {Bayesian, Estimators, {MON}, Math, Mathematics - Statistics Theory, Statistics - Methodology},
}



# License

[MIT](LICENSE)


