Code for SIGGRAPH 2024 (ToG) paper "Specular Polynomials"

A Stata package for bi-variate maps

Bivariate histogram plot from polar coordinates / position vectors in MATLAB.

metaanalysis projects

Fast streaming univariate and bivariate moments and t-statistics

Compute a moving sample Pearson product-moment correlation distance incrementally.

Complex / 2d multivariate bivariate colormap / Domain coloring for Matplotlib, Plotly etc. (pure-numpy)

Compute a sample Pearson product-moment correlation coefficient.

Demonstration of the Gaussian Mixture Model (GMM) for the bivariate normal distribution along with a derivation of the E and M steps.

I Learnt how to make regression for bivariate data in my Statistics class today, So I made a small program to do the same, it takes x and y arrays and outputs a linear equation with slope and constant.

Compute a sample Pearson product-moment correlation distance.

This software package creates subdivision matrices for generalized quadratic and cubic B-spline subdivision for subdivision surfaces as well as subdivision volumes. The subdivision matrices define refinement rules for arbitrary combinatorial structures. The matrices have a valid eigenstructure and a subdominant eigenvalue of 1/2.

Strip Plot, Grouping with Strip Plot, Swarm Plot, Box and Violin Plot, placing plots together, Combining the plots, Joint Plot, Density Plot, Pair Plot

Compute a moving sample Pearson product-moment correlation coefficient incrementally.

Polynomials are mathematical objects which are expressions of variables and coefficients added to-gether. These polynomials can use operator overloading in a very intuitive manner since most of the operators that we can overload are mathematical operators, which can also be applied to polynomials.

Used libraries and functions as follows:

PISA 2012 data visualisation. Univariate, bivariate and multivariate exploration.

Enhance XYZ shop's delivery efficiency by evaluating five delivery services using a comprehensive dashboard to gain insights to improve the selection of delivery service companies.

Animate Bivariate Normal Distribution using JAX and Matplotlib and making it interactive using ipywidgets.

The analysis focused on segmenting a specific group of mall customers using the KMeans unsupervised machine learning algorithm. Univariate, bivariate, and multivariate clusters were identified and analyzed using summary statistics to gain insights into customer behavior and determine the most valuable segment for targeted marketing strategies.