Python micro-package for enhanced statistical analysis
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 and Scikit-learn 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.
- Partial least squares (PLS) regression
- Principal components regression (PCR)
- Subset selection plots
- Additional test statistics in bootstrap resampling
Enhancesa is a result of solutions to exercises in the book Introduction to Statistical Learning 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.
Enhancesa can be installed from the PyPI package repository.
$ pip install enhancesa
>>> 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.
Issues & improvements
- Possible to further reduce dependencies.
boostrapmethod can be improved by adding estimates of more test statistics of interest.
- Use Poetry for package and dependency management, which uses
pyproject.tomlrecommended by PEP 518.
Xinput is a Numpy array.
This package is licensed under an MIT license.
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