A library of smoothing kernels in multiple languages for use in kernel regression and kernel density estimation.
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Updated
Feb 9, 2017
A library of smoothing kernels in multiple languages for use in kernel regression and kernel density estimation.
Assess Balance with Machine Learning
My implementation of some algorithms
Shared Bike Volumn Prediction
Guide for the Baccarelli Lab GitHub
Code and Simulations using Bayesian Approximate Kernel Regression (BAKR)
For quick search
Machine-Learning-Regression
Implementation of various Machine Learning Algorithms and Machine Learning Concepts in Python
Sequential Regression Extrapolation (SRE): An accurate method of extrapolation using machine learning
Identifying the most influential food groups on COVID-19 recovery rate: exploratory data analysis and statistical modeling
Calibration of an air pollution sensor monitoring network in uncontrolled environments with multiple machine learning algorithms
Implementation of a Gaussian Kernel Regression for Temperature prediction using PySpark.
This repo contains an R package to execute ROKET's real data analysis workflow on TCGA cancer types
This R package repository performs optimal transport and kernel regression hypothesis testing. Functions to perform large scale simulations are also provided.
Nonparametric regression examples with R and Python
My realization of kernel regression.
Train a neural network in feature and lazy regimes on a regression task defined on the hyper-sphere.
pwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with some tools extending numpy/scipy. It has a set of powerful parsers and data types for storing calculation data.
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