A library of smoothing kernels in multiple languages for use in kernel regression and kernel density estimation.

Tool for non-parametric curve fitting using local polynomials.

Nonparametric regression examples with R and Python

This R package repository performs optimal transport and kernel regression hypothesis testing. Functions to perform large scale simulations are also provided.

My realization of kernel regression.

Assess Balance with Machine Learning

This repo contains an R package to execute ROKET's real data analysis workflow on TCGA cancer types

Anisotropic smoothing for change-point regression data

My implementation of some algorithms

Guide for the Baccarelli Lab GitHub

Implementation of a Gaussian Kernel Regression for Temperature prediction using PySpark.

Identifying the most influential food groups on COVID-19 recovery rate: exploratory data analysis and statistical modeling

Train a neural network in feature and lazy regimes on a regression task defined on the hyper-sphere.

This is the recent work of my on the importance and application of mathematical function around its Hilbert function theory on artificial intelligence algorithms. The main motivation was the desire of improving the convergence rate and learning rate of various learning algorithms via Generalized Gaussian Radial Basis Function.

Shared Bike Volumn Prediction

Sequential Regression Extrapolation (SRE): An accurate method of extrapolation using machine learning

Implementation of various Machine Learning Algorithms and Machine Learning Concepts in Python

Code and Simulations using Bayesian Approximate Kernel Regression (BAKR)

Calibration of an air pollution sensor monitoring network in uncontrolled environments with multiple machine learning algorithms