Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff

Julia package for writing VTK XML files

Differentiable RayTracing in Julia

A Julia framework for invertible neural networks

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

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

Finite Element tools in Julia

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

Lasso/Elastic Net linear and generalized linear models

A native Julia code for lattice QCD with dynamical fermions in 4 dimension.

Convenience meta-package to load essential packages for statistics

Quantlib implementation in pure Julia

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

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

Journal-style regression tables

System Identification toolbox, compatible with ControlSystems.jl

Uniform Manifold Approximation and Projection (UMAP) implementation in Julia

Beautiful and flexible vizualizations of high dimensional data

Proximal algorithms for nonsmooth optimization in Julia

A Julia package for representing banded matrices

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

Algorithms from circuit theory to predict connectivity in heterogeneous landscapes

Algorithms and data structures for game theory in Julia

Complex neural network examples for Flux.jl

Automatic differentiation of implicit functions

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

Differentiating convex optimization programs w.r.t. program parameters

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 Julia implementation of basic tools for time series analysis compatible with incomplete data.

A Julia implementation of basic tools for time series analysis compatible with incomplete data.

A flexible neural net training library inspired by fast.ai

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.

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Algebraic Multigrid in Julia

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

Climate science package for Julia

A Julia package to construct orthogonal polynomials, their quadrature rules, and use it with polynomial chaos expansions.