[](https://pepy.tech/project/robustbase)
[](https://pepy.tech/project/robustbase/month)
[](https://pepy.tech/project/robustbase/week)
# robustbase
> A Python Library to Calculate Robust Statistical Estimators.
## Installation
OS X, Windows & Linux:
```sh
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](https://rdrr.io/rforge/robustbase/man/Qn.html)
```python
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](https://rdrr.io/rforge/robustbase/man/Sn.html)
```python
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](https://en.wikipedia.org/wiki/Median_absolute_deviation)
```python
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](https://en.wikipedia.org/wiki/Interquartile_range)
```python
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
```python
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:
```sh
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](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](https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/qn_scale.htm)
- [Sn Scale Estimator](https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/sn_scale.htm)
- [Median Absolute Deviation](https://www.statisticshowto.datasciencecentral.com/median-absolute-deviation/)
- [Interquartile Range](https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/interquartile-range/)
Raw data
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"description": "[](https://pepy.tech/project/robustbase)\n[](https://pepy.tech/project/robustbase/month)\n[](https://pepy.tech/project/robustbase/week)\n\n# robustbase\n> A Python Library to Calculate Robust Statistical Estimators.\n\n## Installation\n\nOS X, Windows & Linux:\n\n```sh\npip install robustbase\n```\n## Usage Example\n\nThis package provides functions to calculate the following robust statistical estimators:\n\n* **Qn Scale Estimator**\n * Computes the robust scale estimator Qn, an efficient alternative to the MAD. [Read More](https://rdrr.io/rforge/robustbase/man/Qn.html)\n\n```python\nfrom robustbase.stats import Qn\n\nx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n\n# With bias correction\nres = Qn(x) # result: 3.196183\n\n# Without bias correction\nres = Qn(x, finite_corr=False) # result: 4.43828\n\n```\n\n* **Sn Scale Estimator**\n * Computes the robust scale estimator Sn, an efficient alternative to the MAD. [Read More](https://rdrr.io/rforge/robustbase/man/Sn.html)\n\n```python\nfrom robustbase.stats import Sn\n\nx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n\n# With bias correction\nres = Sn(x) # result: 3.5778\n\n# Without bias correction\nres = Sn(x, finite_corr=False) # result: 3.5778\n\n```\n\n* **Median Absolute Deviation (MAD)**\n * Compute the MAD, a robust measure of the variability of a univariate sample of quantitative data. [Read More](https://en.wikipedia.org/wiki/Median_absolute_deviation)\n\n```python\nfrom robustbase.stats import mad\n\nx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nres = mad(x)\n\n```\n* **Interquartile Range (IQR)**\n * Compute the interquartile range, a measure of statistical dispersion, or spread. [Read More](https://en.wikipedia.org/wiki/Interquartile_range)\n\n```python\nfrom robustbase.stats import iqr\n\nx = [1, 2, 3, 4, 5]\nres = iqr(x)\n```\n\n* **Co-Median Location and Scatter \"Covariance\" Estimator**\n * Compute the multivariate \"Comedian\" covariance, a robust measure of multivariate location and scatter. Read More\n\n```python\nfrom robustbase.stats import covComed\n\n# Example data matrix\nX = np.random.rand(100, 3)\n\n# Compute the Co-Median covariance estimator\nresult = covComed(X)\n\n# Access the components of the result\nprint(\"Covariance Matrix:\", result.cov)\nprint(\"Center:\", result.center)\nprint(\"Weights:\", result.weights)\n```\n\n## Development Setup\n\nFor local development setup:\n\n```sh\ngit clone https://github.com/deepak7376/robustbase\ncd robustbase\npip install -r requirements.txt -r requirements-dev.txt\n```\n\n## Recent Changes\n\n### Version 3.0.0\n- Changed the API's call\n- Refactored the dir structure\n- Updated README with usage examples for all functions.\n\n\n## Contributing\n\n1. Fork it ([https://github.com/deepak7376/robustbase/fork](https://github.com/deepak7376/robustbase/fork))\n2. Create your feature branch (`git checkout -b feature/fooBar`)\n3. Commit your changes (`git commit -am 'Add some fooBar'`)\n4. Push to the branch (`git push origin feature/fooBar`)\n5. Create a new Pull Request\n\n## References\n- [Qn Scale Estimator](https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/qn_scale.htm)\n- [Sn Scale Estimator](https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/sn_scale.htm)\n- [Median Absolute Deviation](https://www.statisticshowto.datasciencecentral.com/median-absolute-deviation/)\n- [Interquartile Range](https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/interquartile-range/)\n",
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