Core functionality for the MLJ machine learning framework

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

Export Julia profiles to the pprof format

Differentiable RayTracing in Julia

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

Julia port of L.J.P. van der Maaten and G.E. Hintons T-SNE visualisation technique.

ACME.jl - Analog Circuit Modeling and Emulation for Julia

Convenience meta-package to load essential packages for statistics

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

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

Algorithms and data structures for game theory in Julia

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

A flexible neural net training library inspired by fast.ai

Perceptually Uniform Colour Maps for Julia

A package for Counterfactual Explanations and Algorithmic Recourse 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.

Evaluating Robustness of Neural Networks with Mixed Integer Programming

Combinatorial optimization layers for machine learning pipelines

A package for time series data processing, classification, clustering, and prediction.

Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.

Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.

Fast and automatic structural identifiability software for ODE systems

MADS: Model Analysis & Decision Support

Yet Another XArray-like Julia package

Sleek implementations of the ZigZag, Boomerang and other assorted piecewise deterministic Markov processes for Markov Chain Monte Carlo including Sticky PDMPs for variable selection

Dynamic Time Warping (DTW) and related algorithms in Julia, at Julia speeds

A framework for simulating viscous incompressible flows about arbitrary body shapes.

Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.

Julia for Whole-genome Analysis Software

A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language

Julia implementation for various Frank-Wolfe and Conditional Gradient variants

Beta Machine Learning Toolkit

Bayesian optimization for Julia

Algorithms that combine tensor network methods with automatic differentiation

Dive deeper into your data with interactive graphics