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        "author": "Ali Sina",
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            "Development Status :: 2 - Pre-Alpha",
            "Framework :: IPython",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Visualization"
        ],
        "description": "# enhancesa\n\n[![Build Status](https://travis-ci.org/alisiina/enhancesa.svg?branch=master)](https://travis-ci.org/alisiina/enhancesa)\n[![codecov](https://codecov.io/gh/alisiina/enhancesa/branch/master/graph/badge.svg)](https://codecov.io/gh/alisiina/enhancesa)\n[![Total alerts](https://img.shields.io/lgtm/alerts/g/alisiina/enhancesa.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/alisiina/enhancesa/alerts/)\n[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/alisiina/enhancesa.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/alisiina/enhancesa/context:python)\n[![Documentation Status](https://readthedocs.org/projects/enhancesa/badge/?version=latest)](https://enhancesa.readthedocs.io/en/latest/?badge=latest)\n![repo size](https://img.shields.io/github/repo-size/alisiina/enhancesa.svg?color=9cf)\n[![license](https://img.shields.io/github/license/alisiina/enhancesa.svg?color=blueviolet)](https://opensource.org/licenses/MIT)\n\n\nEnhancesa 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. \n\nFor 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.\n\n\n#### Upcoming features\n\n* Partial least squares (PLS) regression\n* Principal components regression (PCR)\n* Subset selection plots\n* Additional test statistics in bootstrap resampling\n\n\n### Motivation\n\nEnhancesa 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.\n\n\n### Installation\n\nEnhancesa can be installed from the [PyPI](https://pypi.org/project/enhancesa/) package repository.\n\n```\n$ pip install enhancesa\n```\n\n\n### Quick glimpse\n\n```python\n>>> import numpy as np\n>>> import enhancesa as esa\n>>> # Create some dummy data\n>>> x = np.random.normal(size=100)\n>>> # Compute test statistics with bootstrap resampling\n>>> esa.bootstrap(x, iters=1000)\nEstimated mean: -0.025309\nEstimated SE: 0.095531\ndtype: float64\n```\nFind out more about the full set of features in the [documentation](https://enhancesa.readthedocs.io/en/latest/?badge=latest).\n\n\n### Issues & improvements\n\n* Possible to further reduce dependencies.\n* `boostrap` method can be improved by adding estimates of more test statistics of interest.\n* Use [Poetry](https://poetry.eustace.io/) for package and dependency management, which uses\n`pyproject.toml` recommended by [PEP 518](https://www.python.org/dev/peps/pep-0518/).\n* `enhancesa.SubsetSelect` will give `NotImplemented` error if `X` input is a Numpy array.\n\n\n### License\n\nThis package is licensed under an [MIT](https://github.com/alisiina/enhancesa/blob/master/LICENSE.txt) license.\n\n\n",
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