Bayesian inference with probabilistic programming.

A Julia machine learning framework

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Distributed High-Performance Symbolic Regression in Julia

Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.

Boosted trees in Julia

Implementations of the models from the Statistical Rethinking book with Turing.jl

Core functionality for the MLJ machine learning framework

Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Exploratory analysis of Bayesian models with Julia

MagNav: airborne Magnetic anomaly Navigation

Julia FFI interface to Microsoft's LightGBM package

Beta Machine Learning Toolkit

A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.

Stochastic Optimization, Learning, Uncertainty and Sampling

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

Home of the MLJ model registry and tools for model queries and mode code loading

Fast, scalable and flexible Outlier Detection with Julia

High level functions for analyzing the output of simulations

Bayesian Generalized Linear models using `@formula` syntax.

Econometrics in Julia

Turing + Makie = Turkie

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Flexible and efficient persistent homology computation.

The automatically differentiable and GPU-compatible toolkit for CMB analysis.

Parallel & lightning fast implementation of available classic and contemporary variants of the KMeans clustering algorithm

Implementations of single and multi-ellipsoid nested sampling

Implementation of a Partial Least Squares Regressor

Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks.

Comparing performance and results of mcmc options using Julia

Sole.jl – Long live transparent modeling!

Examples for Bayesian inference using DynamicHMC.jl and related packages.

TreeParzen.jl, a pure Julia hyperparameter optimiser with MLJ integration

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