A New Interactive Approach to Learning Data Analysis

Python implementation of an extension of the Kolmogorov-Smirnov test for multivariate samples

about statistical techniques for Data Science

Very large scale classification based on K-Means clustering & Multi-Kernel SVM(SimpleMKL), Soft Computing article, June 2019, https://dl.acm.org/doi/abs/10.1007/s00500-018-3041-0

XeroGraph is a Python package developed for researchers and data scientists to analyze, visualize and impute missing data in datasets.

Testing a self implemented LCG vs. C# standard through the Kolmogorow Smirnow test & and a Runs Test

Matlab implementation of the improved Peacock algorithm by Yuanhui Xiao.

A simple, vectorized implementation of the two-sample Kolmogorov-Smirnov test in PyTorch.

R functions mainly for computations related to Kolmogorov-Smirnov tests

This is a homework I did in the Spring 2017. It involves conducting Chi Square Tests, Confidence Intervals, Kolmogorov-Smirnov Tests, and Shapiro-Wilk Normality Tests. It has problem numbers that are associated to problems in "Using R: Introductory Statistics".

Code for fitting EEG data with Wishart and t-Wishart distributions

ICG (Inversive Congruential Generator), MSM (Middle Square Method), LFG (Lagged Fibonacci Generator), MRG (Multiple Recursive Generators), KS Test (Kolmogorov-Smirnov Test)

The salary dataset contains info on 474 Midwestern bank employees. Tasks include understanding the dataset's structure, summarizing numerical variables, testing hypotheses on salary equality, gender-based differences, age group analysis, and proportion comparison.

ClusterTrack software allowed for the first time comprehensive measurements of microtubule behavior, including during stages of depolymerization, for which there is no known molecular marker