A collection of structures to define observation or action spaces of Reinforcement Learning environments. [May be moved into CommonRLInterface once stable]

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Toolbox for categorical time-series analysis.

Bayesian addition to Flux.jl

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Julia implementation of Autocorrelation Wavelets.

Tree data structures for Fast Multipole Methods and H-Matrices

Solutions for combinatorial problems using symbolic methods.

Algorithms for performing angular differential imaging (ADI)

The base package for AutoMLPipeline.jl and TSML.jl

A Julia package for modelling Algorithmic Recourse Dynamics.

A companion package for Microbiome.jl for working with the Biobakery family of computational tools

Alpha stable and sub-Gaussian distributions in Julia

A framework to define and output observables and statistics from CliMA simulations

A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)

Causal Inference using Gaussian Processes with Structured Latent Confounders. Estimate treatment effects with Gaussian processes.

Compute Bell inequalities and their quantum violations.

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Tests for constraint-based metabolic models

Tools for generating and visualizing planar maps

A Julia package for piecewise spectral methods such as p-FEM

A simple package for easily downloading/parsing CPS microdata files.

Low-cost linear algebra functions for matrices with units

High performance hybrid Monte Carlo simulation

A Makie theme

Perform image hashing in julia

🌊 D-Wave Neal Simulated Annealing Interface for JuMP

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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

Generates a cellular automata animation of the given GitHub repository.

Julia implementation of the Taylor-Fourier Transform (TFT)

A Julia library to resample timeseries (from TimeSeries.jl)

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Julia package for MPI reconstruction

Calculates multidimensional functions faster by exploiting their separability.

Provides a general framework for implementing and performing Kernel Density Estimation

Stochastic distorted-wave Born approximation for acoustic scattering, in Julia

Symmetry-allowed k ⋅ p expansions

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