enhancesa 0.1a0
Python micro-package for enhanced statistical analysis
License: MIT
Requires Python: >=3.6
Keywords: statistics mathematics plotting diagnostics analysis

# enhancesa

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Enhancesa is a collection of tools for a better and more simplified statistical analysis in Python. It primarily aids in manual analysis and prediction tasks that use packages like [Statsmodels](https://www.statsmodels.org/stable/index.html) and [Scikit-learn](https://scikit-learn.org/stable/index.html) in their workflow. 

For example, Enhancesa provides answers to questions like: Which subset of features gives me the lowest error rate in an ordinary least squares model? What are estimates of population mean and standard deviation using bootstrap resampling? And etc.


#### Upcoming features

* Partial least squares (PLS) regression
* Principal components regression (PCR)
* Subset selection plots
* Additional test statistics in bootstrap resampling


### Motivation

Enhancesa is a result of solutions to exercises in the book [Introduction to Statistical Learning](https://www-bcf.usc.edu/~gareth/ISL/) by the Tibshirani et al. When going through the exercises, I found Python, unlike R, lacking in providing convenient functionalities. *At this stage*, this package is simply a collection of functions I used in my solutions to exercises in the book.


### Installation

Enhancesa can be installed from the [PyPI](https://pypi.org/project/enhancesa/) package repository.

```
$ pip install enhancesa
```


### Quick glimpse

```python
>>> import numpy as np
>>> import enhancesa as esa
>>> # Create some dummy data
>>> x = np.random.normal(size=100)
>>> # Compute test statistics with bootstrap resampling
>>> esa.bootstrap(x, iters=1000)
Estimated mean: -0.025309
Estimated SE: 0.095531
dtype: float64
```
Find out more about the full set of features in the [documentation](https://enhancesa.readthedocs.io/en/latest/?badge=latest).


### Issues & improvements

* Possible to further reduce dependencies.
* `boostrap` method can be improved by adding estimates of more test statistics of interest.
* Use [Poetry](https://poetry.eustace.io/) for package and dependency management, which uses
`pyproject.toml` recommended by [PEP 518](https://www.python.org/dev/peps/pep-0518/).
* `enhancesa.SubsetSelect` will give `NotImplemented` error if `X` input is a Numpy array.


### License

This package is licensed under an [MIT](https://github.com/alisiina/enhancesa/blob/master/LICENSE.txt) license.