Julia package with tools at the cross-section of Power System Operations, Applied Mathematics, High-Performance Computations, and Machine-Learning.

Differentiable wrapper for FrankWolfe.jl convex optimization routines

Easy-to-use biological sequence tools working with DNA, RNA and aminoacid sequences.

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Physical vector and particle types for Julia

A collection of functions to analyze phonetic data

Monotonic cubic interpolation in Julia

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WAIC and PSIS model comparison methods as explained in Statistical Rethinking.

Neural-Network Outlier Detection Algorithms for Julia

A package for representing orthogonal polynomials as quasi arrays

Julia wrapper for SDPLR

Optimal Transport Networks in Spatial Equilibrium - in Julia

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Julia package for Determinantal Point Processes

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Simple extension to Distributions.jl to incorporate interpolated PDFs

Distributions for continuous variables

Engineering, Economic, and Environmental Electricity Simulation Tool (E4ST)

A package for obtaining the effective time-averaged Lindbladian.

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

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PAGE-2020 - a Julia implementation of the PAGE Integrated Assessment Model

Split and recombine your data

Tools for working with ITS_LIVE glacier velocity data

Image smoothing algorithms

Sequences basics

Makie plots with legends and labels and everything

Julia Implementation of the nonintrusive model reduction approaches: Operator Inference (OpInf) and Lift & Learn (LnL)

A julia Bokeh server

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Controllers for airborne wind energy systems

Support for reading and using pseudopotentials in Julia

Provides a Channel where elements are associated with a priority. take! returns highest priority item.

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.

Partially ordered sets fully compatible with Graphs.jl

Statistical inference for Poisson Processes