fabsig

GPBoost

Combining tree-boosting with Gaussian process and mixed effects models

KTBoost

A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

GradientNewtonBoosting

Comparing gradient and Newton boosting

Compare_ML_HighCardinality_Categorical_Variables

Machine Learning Methods for High-Cardinality Categorical Data

CreateDummyVariablesSPSS

Create dummy variables in SPSS with Python 3 support for SPSS version 27 and latter

ROC_PrecisionRecall

spate

spate: Spatio-Temporal Modeling of Large Data Using a Spectral SPDE Approach

AirBnbPricePrediction

Training and Testing a Set of Machine Learning/Deep Learning Models to Predict Airbnb Prices for NYC

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Comparison_GLMM_Packages

Comparing Software Packages for Generalized Linear Mixed Effects Models (GLMMs)

glmmnet

glmmnet_experiments_gpboost

LightGBM

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

shap

A game theoretic approach to explain the output of any machine learning model.

spectra

A header-only C++ library for large scale eigenvalue problems

iterativeVL

This repository contains the R code for the simulations in the paper "Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models".

lmmnn

ML_models_for_extrapolation

treeshap

Compute SHAP values for your tree-based models using the TreeSHAP algorithm