Ball Python Package
The fundamental problems for data mining and statistical analysis are:
- Whether distributions of two samples are distinct?
- Whether two random variables are dependent?
Ball package provides solutions for these issues. Moreover, a variable screening (or feature screening) procedure is also implemented to tackle ultra high dimensional data. The core functions in Ball package are bd_test, bd, bcov_test, bcov, bcorsis and bcor.
These functions based on ball statistic have several advantages:
- It’s applicable to univariate and multivariate data in Banach space.
- There is no need for moment assumption, which means that outliers and heavy-tail data are no longer a problem.
- They perform well in many setting without complex adjustments for parameters.
Particularly, for two-sample or K-sample problem, bd_test has been proved to cope well for imbalanced data, and bcov_test and bcorsis work well for detecting the relationship between complex responses and/or predictors, such as shape, compositional as well as censored data.
We recommend that compile C++ files by gcc instead of Visual Studio. You could download MinGW (https://sourceforge.net/projects/mingw/) and then add the path MinGW/bin to system environment variable “path”.
Anaconda3 is needed, and the version should be greater than 3.4. You should add all the related path of Anaconda3 to system environment variable “path”, as well as the path of MinGW/bin.
Then you can pip install the Ball package!
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