A package for obtaining the effective time-averaged Lindbladian.

Distributions for continuous variables

-

A package for Random Matrix Theory

A Julia package for real-time audification of ODEs and SDEs

Split and recombine your data

RELOG: Reverse Logistics Optimization

A stack implementation with a reset! function which avoids garbage collection for scientific machine learning (SciML)

Tools for robot visualization in Julia.

Robust SVD and PCA in Julia

Julia wrapper for SDPLR

A Julia repository for semiclassical orthogonal polynomials

A Package for Estimating Stochastic Frontier Models using Julia

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

TensorNetworkCodes is a Julia library developed to support the following research: https://arxiv.org/abs/2109.11996

-

-

⚛️

Just the Comrade Models

Simulate EEG / ERP data with overlap, non-linear effects, multiple regression

Julia package to seamlessly integrate Mathematica's MathLink with Julia's native Symbolics package

Naive generic implementation of tensor networks (WIP)

-

-

Sparse matrices in coordinate format for Julia

A Julia package for reading/writing wannier90 file formats

-

Reference single time step propagators. Part of the COFII framework.

Library for simulating time binned photons in Waveguide Quantum Electrodynamics

Open-source time-Temperature inversion of thermochronometric data

Julia library for fitting and analyzing state space models. Provides efficient implementations of various SSMs including the canonical Gaussian LDS (Kalman Filter/Smoother), Poisson LDS, HMMs, etc.

Stan versions of StatisticalRethinking models.

Makie plots with legends and labels and everything

A Julia package for computing singular integrals

Julia implementation of the Taylor-Fourier Transform (TFT)

A simple workflow engine powered by Julia

Data processor for Vlasiator

Voronoi diagrams in N dimensions using an improved raycasting method.

Numerical Methods for Vlasov-Poisson and Vlasov-Maxwell Systems

Ground-motion simulation via the Stochastic Method