Welcome to enhancesa’s documentation!

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Enhancesa is a collection of tools for 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.


Enhancesa can be installed from the PyPI package repository.

$ pip install enhancesa

Alternatively, you can download it from the source on Github.

Quick glimpse

>>> 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

Upcoming features

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


This package is licensed under an MIT license.