A Python Library to Calculate Robust Statistical Estimators.
OS X, Windows & Linux:
pip install robustbaseThis 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)For local development setup:
git clone https://github.com/deepak7376/robustbase
cd robustbase
pip install -r requirements.txt -r requirements-dev.txt- Changed the API's call
- Refactored the dir structure
- Updated README with usage examples for all functions.
- Fork it (https://github.com/deepak7376/robustbase/fork)
- Create your feature branch (
git checkout -b feature/fooBar) - Commit your changes (
git commit -am 'Add some fooBar') - Push to the branch (
git push origin feature/fooBar) - Create a new Pull Request