Scalable and user friendly neural :brain: forecasting algorithms.

🎓 Tidy tools for academics

Flexible Statistics and Data Analysis (FSDA) extends MATLAB for a robust analysis of data sets affected by different sources of heterogeneity. It is open source software licensed under the European Union Public Licence (EUPL). FSDA is a joint project by the University of Parma and the Joint Research Centre of the European Commission.

Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)

A Julia package for robust regressions using M-estimators and quantile regressions

Companion package to the 2nd edition of the book "Robust Statistics: Theory and Methods"

Robust Regression for arbitrary non-linear functions

Robust locally weighted multiple regression in Python

a c++ library with statistical machine learning algorithms for linear and non-linear robust regression that can be used with python.

Robust regression algorithm that can be used for explaining black box models (Python implementation)

Robust regression algorithm that can be used for explaining black box models (R implementation)

Different type of solvers to solve systems of nonlinear equations

Weighted BACON algorithms

DecoR: Deconfounding Time Series with Robust Regression

Classification of Alan Miller's Fortran codes for statistics and numerical methods and copies of them

Unveiling the Art of Stock Market Prognostication through Regression Algorithms. Delve into our research exploring the power of machine learning in predicting market trends. Discover the secrets behind top regression models like Linear, Robust, Ridge, and Lasso Regression. Unravel the complexities of the market with data-driven precision.

Random Sample Consensus (RANSAC) Python Implementation

Robust shape fitting

Simple 1d robust regression with huber loss in the case of anomalies / outliers

universal rank-order method to analyze noisy data

Fortran 90 library of John Burkardt for regression using least-squares and other criteria, based on Spaeth's codes

2019.12.18 개인프로젝트. 통신사 고객들의 이탈 예측

Regression Analysis Utility

Analysis and comparison of Weighted Least Squares (WLS) and Ordinary Least Squares (OLS). Covers heteroscedasticity, Feasible WLS, Huber robust regression, and validates methods with a Monte Carlo simulation

📊 Explore weighted least squares regression, compare it with ordinary least squares, and examine methods for handling heteroscedasticity effectively.