Core algorithms and data for X-ray microanalysis calculations

WGPU graphics library julia

Information Theoretic Inequalities

Entropic Cone approximation and optimization

Modular tracking algorithm for various GNSS Systems

Metric Gaussian Variational Inference

Ship maneuvering simulation tool with respect to ShipMMG model

-

Neural-Network Outlier Detection Algorithms for Julia

Quick and dirty implementation of the Les Houches Event Format (LHE Files) in Julia

Just the Comrade Models

Implementation of a GNSSReceiver

Alignment and comparison of small molecules read from .sdf files represented as Gaussian Mixture Models.

Tools for robot visualization in Julia.

-

DNA Motif discovery that includes the discovery of flexible (long or gapped) motifs.

Built-in solvers for the GeoStats.jl framework

Split and recombine your data

A package for modeling and solving Quadratic Program Networks.

Collection 3D visualization tools associated with the Caesar.jl and RoME.jl robot navigation packages

(Hierarchical) Phylogenetic Models in Julia

-

Yet another package for computing multidimensional non-uniform fast Fourier transforms (NUFFTs) in Julia

Makie plots with legends and labels and everything

-

Locally weighted regression solver for the GeoStats.jl framework

Controllers for airborne wind energy systems

Physical vector and particle types for Julia

-

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

A collection of functions to analyze phonetic data

A backend independent shader API (For e.g. OpenGL WebGL and Vulkan)

-

Compute Bell inequalities and their quantum violations.

-

Package for fitting models according to the deep compartment modeling framework for pharmacometric applications.

Hypothesis tests of calibration.

Unfortunately, FMUs (fmi-standard.org) are not differentiable by design. To enable their full potential inside Julia, FMISensitivity.jl makes FMUs fully differentiable, regarding to: states and derivatives | inputs, outputs and other observable variables | parameters | event indicators | explicit time | state change sensitivity by event

-

Bayesian addition to Flux.jl