Maps from the line into spaces

Interfaces for deep learning data pipelines.

A Julia package for reading and writing LAS data

Perform image hashing in julia

A Julia package for modelling Algorithmic Recourse Dynamics.

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Modular Julia-based agent framework to implement multi-agent systems

Package for unfolding of complex 3-D geometries.

Repository for plotting implicitly defined surfaces and curves in R3.

Low-cost linear algebra functions for matrices with units

🌊 D-Wave Neal Simulated Annealing Interface for JuMP

Fast Dispersion Measure Transform implemented in Julia

Digital signal processing for radiation detectors

Implements the Partitioned Least Squares algorithms

Pure Julia implementation of the OMOP Common Data Model (CDM)

Quantum Exact Simulation Toolkit is a high performance simulator of quantum circuits, state-vectors and density matrices

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Independent Hypothesis Weighting for multiple testing with side-information in Julia

Julia package for visualizing chords on the guitar fretboard.

Use PETSc solvers in sequential and parallel Julia runs

Tools to specify and simulate individual based models, using applied category theory

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Data processor for Vlasiator

Many-body objects for quantum computing: a Julia implementation

Dynamic Gradients for Quantum Control

Entire Efficient Frontier by Status-Segment Method

Numerical Methods for Vlasov-Poisson and Vlasov-Maxwell Systems

Http://proceedings.mlr.press/v119/bruinsma20a.html

An unofficial implementation of publicly available approximated polynomial models for NASA's Generic Transport Model aircraft.

Linear fractional programming with Julia and JuMP

Julia package for progress metering using the file system.

Core utilities for PopGen.jl, including the PopData type and file IO

The base package for AutoMLPipeline.jl and TSML.jl

A Makie theme

Alpha stable and sub-Gaussian distributions in Julia

GraphBLAS backed graphs for LightGraphs.jl

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Julia Implementation of Integral Arrays

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