Core functionality for the MLJ machine learning framework

Implementation of domain-specific language (DSL) for dynamic probabilistic programming

Automatic Finite Difference PDE solving with Julia SciML

A meta package for data science in julia

Differentiable RayTracing in Julia

A Julia framework for invertible neural networks

Math with more good bits

Algorithms for quantifying associations, independence testing and causal inference from data.

Algorithms for quantifying associations, independence testing and causal inference from data.

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

A development environment for robust and global optimization

Lasso/Elastic Net linear and generalized linear models

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

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

Gaussian Process package based on data augmentation, sparsity and natural gradients

System Identification toolbox, compatible with ControlSystems.jl

Algorithms from circuit theory to predict connectivity in heterogeneous landscapes

A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

Julia wrapper for the Voyager data exploration tool

ODE integration using Taylor's method, and more, in Julia

Library for finding the roots of a function using interval arithmetic

A Julia package dedicated to simulating quantum many-body systems using Matrix Product States (MPS)

Algorithms and data structures for game theory in Julia

Complex neural network examples for Flux.jl

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

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

Solve non-linear HJB equations.

A flexible neural net training library inspired by fast.ai

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

A Julia package for the creation, manipulation and analysis of the structure, dynamics and functions of multilayer graphs.

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Perceptually Uniform Colour Maps for Julia

Algebraic Multigrid in Julia

Climate science package for Julia

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

Causal.jl - A modeling and simulation framework adopting causal modeling approach.

PortAudio wrapper for the Julia programming language, compatible with the JuliaAudio family of packages

State estimation, smoothing and parameter estimation using Kalman and particle filters.