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

Convenience meta-package to load essential packages for statistics

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.

Fermi quantum chemistry program

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

System Identification toolbox, compatible with ControlSystems.jl

The Base interface of the SciML ecosystem

Simple multithreading in julia

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

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

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

Julia package for simulating Dynamics on Networks

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.

Arrays which also have a label for each element for easy scientific machine learning (SciML)

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 package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Algebraic Multigrid in Julia

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

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

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

Combinatorial optimization layers for machine learning pipelines

A standard library of components to model the world and beyond

Equation-based modeling and simulations in Julia

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.

Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.

Using Vulkan from Julia

Utilities and abstractions for Machine Learning tasks

Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.

Package for dynamical modeling of power grids