robustbase 3.1.0

A Python Based Library to Calculate Estimators (Sn, Qn, MAD, IQR, covComed)

robustbase

A Python Library to Calculate Robust Statistical Estimators.

Installation

OS X, Windows & Linux:

pip install robustbase

Usage Example

This package provides functions to calculate the following robust statistical estimators:

• Qn Scale Estimator
  • Computes the robust scale estimator Qn, an efficient alternative to the MAD. Read More

from robustbase.stats import Qn

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# With bias correction
res = Qn(x)  # result: 3.196183

# Without bias correction
res = Qn(x, finite_corr=False)  # result: 4.43828

• Sn Scale Estimator
  • Computes the robust scale estimator Sn, an efficient alternative to the MAD. Read More

from robustbase.stats import Sn

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# With bias correction
res = Sn(x)  # result: 3.5778

# Without bias correction
res = Sn(x, finite_corr=False)  # result: 3.5778

• Median Absolute Deviation (MAD)
  • Compute the MAD, a robust measure of the variability of a univariate sample of quantitative data. Read More

from robustbase.stats import mad

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
res = mad(x)

• Interquartile Range (IQR)
  • Compute the interquartile range, a measure of statistical dispersion, or spread. Read More

from robustbase.stats import iqr

x = [1, 2, 3, 4, 5]
res = iqr(x)

• Co-Median Location and Scatter "Covariance" Estimator
  • Compute the multivariate "Comedian" covariance, a robust measure of multivariate location and scatter. Read More

from robustbase.stats import covComed

# Example data matrix
X = np.random.rand(100, 3)

# Compute the Co-Median covariance estimator
result = covComed(X)

# Access the components of the result
print("Covariance Matrix:", result.cov)
print("Center:", result.center)
print("Weights:", result.weights)

Development Setup

For local development setup:

git clone https://github.com/deepak7376/robustbase
cd robustbase
pip install -r requirements.txt -r requirements-dev.txt

Recent Changes

Version 3.0.0

• Changed the API's call
• Refactored the dir structure
• Updated README with usage examples for all functions.

Contributing

1. Fork it (https://github.com/deepak7376/robustbase/fork)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request

References

• Qn Scale Estimator
• Sn Scale Estimator
• Median Absolute Deviation
• Interquartile Range