[](https://pepy.tech/project/robustbase)
[](https://pepy.tech/project/robustbase/month)
[](https://pepy.tech/project/robustbase/week)
# robustbase
> A Python Library to Calculate Estimators.
## Installation
OS X , Windows & Linux:
```sh
pip install robustbase
```
## Usage example
This package is used to calculate the following statistical estimators.
* **Qn scale estimator**
* Compute the robust scale estimator Qn, an efficient alternative to the MAD. [Read More.](https://rdrr.io/rforge/robustbase/man/Qn.html)
```python
Qn(x, constant = 2.21914, finite_corr=True)
```
```python
from robustbase import Qn
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# with bias correction
res = Qn(x) # ans = 3.196183
# Without bias correction
res = Qn(x, finite_corr=False) # ans = 4.43828
```
* **Sn scale estimator**
* Compute the robust scale estimator Sn, an efficient alternative to the MAD.[Read More.](https://rdrr.io/rforge/robustbase/man/Sn.html)
```python
Sn(x, constant = 1.1926, finite_corr=True)
```
```python
from robustbase import Sn
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# with bias correction
res = Sn(x) # ans = 3.5778
# Without bias correction
res = Sn(x, finite_corr=False) # ans = 3.5778
```
* **Median Absolute Deviation(MAD)**
```python
mad(x, center = None, constant = 1.4826, na = False,
low = False, high = False)
```
```python
from robustbase import mad
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
res = mad(x)
```
* **Interquartile Range (IQR)**
```python
iqr(x)
```
```python
from robustbase import iqr
x = [1, 2, 3, 4. 5]
res = iqr(x)
```
## Development setup
For local development setup
```sh
git clone https://github.com/deepak7376/robustbase
cd robustbase
pip install -r requirements.txt
```
## Meta
Deepak Yadav – [@imdeepak_dky](https://twitter.com/imdeepak_dky) – dky.united@gmail.com
Distributed under the MIT license. See ``LICENSE`` for more information.
[https://github.com/deepak7376/robustbase/blob/master/LICENSE](https://github.com/deepak7376)
## 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
https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/qn_scale.htm
https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/sn_scale.htm
https://www.statisticshowto.datasciencecentral.com/median-absolute-deviation/
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# robustbase\n> A Python Library to Calculate Estimators.\n\n## Installation\n\nOS X , Windows & Linux:\n\n```sh\npip install robustbase\n```\n## Usage example\n\nThis package is used to calculate the following statistical estimators.\n\n* **Qn scale estimator**\n * Compute the robust scale estimator Qn, an efficient alternative to the MAD. [Read More.](https://rdrr.io/rforge/robustbase/man/Qn.html)\n```python\nQn(x, constant = 2.21914, finite_corr=True)\n```\n\n```python\nfrom robustbase import Qn\n \nx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n\n# with bias correction\nres = Qn(x) # ans = 3.196183\n\n# Without bias correction\nres = Qn(x, finite_corr=False) # ans = 4.43828\n\n```\n\n* **Sn scale estimator**\n * Compute the robust scale estimator Sn, an efficient alternative to the MAD.[Read More.](https://rdrr.io/rforge/robustbase/man/Sn.html)\n\n```python\nSn(x, constant = 1.1926, finite_corr=True)\n\n```\n\n```python\nfrom robustbase import Sn\n \nx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n\n# with bias correction\nres = Sn(x) # ans = 3.5778 \n\n# Without bias correction\nres = Sn(x, finite_corr=False) # ans = 3.5778\n\n```\n\n* **Median Absolute Deviation(MAD)**\n\n```python\nmad(x, center = None, constant = 1.4826, na = False,\n low = False, high = False)\n```\n\n```python\nfrom robustbase import mad\n\nx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nres = mad(x)\n\n```\n* **Interquartile Range (IQR)**\n\n```python\niqr(x)\n```\n\n```python\nfrom robustbase import iqr\n\nx = [1, 2, 3, 4. 5]\nres = iqr(x)\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\n```\n\n## Meta\n\nDeepak Yadav \u2013 [@imdeepak_dky](https://twitter.com/imdeepak_dky) \u2013 dky.united@gmail.com\n\nDistributed under the MIT license. See ``LICENSE`` for more information.\n\n[https://github.com/deepak7376/robustbase/blob/master/LICENSE](https://github.com/deepak7376)\n\n## Contributing\n\n1. Fork it (<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\nhttps://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/qn_scale.htm\nhttps://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/sn_scale.htm\nhttps://www.statisticshowto.datasciencecentral.com/median-absolute-deviation/\nhttps://www.statisticshowto.datasciencecentral.com/probability-and-statistics/interquartile-range/\n",
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