MeshGraphNets.jl is a software package for the Julia programming language that provides an implementation of the MeshGraphNets framework by Google DeepMind for simulating mesh-based physical systems via graph neural networks.

DynamicHMC versions of StatisticalRethinking models

Julia utilities for handling VCF files

Transition Indicators / Early Warning Signals / Regime Shifts / Change Point Detection

Differential-Independence Mixture Ensemble (DIME) MCMC sampling for Julia

Bayesian inference on wiring diagrams.

A package for temporal point process modeling, simulation and inference (unmaintained)

GPT-3 as a generative function in Gen.

From Semiconductor to TransistorLevel Modeling in Julia

Bayesian Information Gap Decision Theory

Julia's HPC command for automatic feature/model selection using all-subset-regression approaches

Methods for profile likelihood analysis.

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Nonlinear regression in Julia

WIP: Wrapper package for the sample method in Stan's cmdstan executable.

Gaussian distributions as state variables and for uncertainty quantification with Unitful

Implementation of the MLJ scientific type convention

Analysis of Single Cell Expression data in Julia

Tools for chemometrics and machine learning on high-dimensional data (e.g. Partial least squares regression/discrimination)

Solving unregularized optimal transport problems with Julia

Integrating Neural Ordinary Differential Equations, the Method of Lines, and Graph Neural Networks

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Julia package for using Jags as an external program

Empirical Bayes estimation and inference in Julia.

High-fidelity orbit determination and propagation for Near-Earth Objects, in Julia

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data

Design scalable noise characterisation experiments for quantum computers

Supports approximating functions and solving differential equations on various higher dimensional domains such as disks and triangles

Uniform interface for machine learning in Julia

Working efficiently with datasets where observations have associated uncertainties.

Surrogate modeling package in Julia using radial basis functions for use in optimisation.

Representing machine learning models using mathematical programming

Simulators for kite power systems

Implementation of the AIControl paper in Julia 1.0

✂️ Pooled CRISPR screen optimization tool

Source code for https://nature.com/articles/s41467-019-10933-3

Continuous transformations from ℝ or ℝⁿ to various open sets

Package for approximation of solutions to matrix differential equations or time-dependent matrices via dynamically evolving low rank decomposition.

A Julia package that implements a category of reaction (transportation) network-type dynamical systems.

An unofficial Julia wrapper for the RAPIDS.ai ecosystem using PythonCall.jl