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Definitions of the Julia types for simulating an ISO's market clearing.

Reinforcement learning framework to find and analyze the likeliest failures of a system under test.

Quantum mechanical few body systems in Julia

Microscope Point Spread Function Calculator

Https://stevengogogo.github.io/FindSteadyStates.jl/dev

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

Tools for generating and visualizing planar maps

Hypothesis tests of calibration.

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Implementation of a drone surveillance problem with POMDPs.jl

Non-compartmental pharmacokinetics analysis for Julia.

Drawing functions for `SimpleGraphs`

A Julia package for modelling Algorithmic Recourse Dynamics.

A Makie theme

An interface for solving a stochastic capacity expansion problem via a Dantzig-Wolfe decomposition algorithm

Geometry optimization for molecular simulation

Point-based value iteration solver for POMDPs

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A package that stores Constraint Programming models for JuliaConstraints solvers

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

Compute Bell inequalities and their quantum violations.

Interfaces for deep learning data pipelines.

A custom branch-and-bound algorithm for solving low-rank matrix completion to certifiable optimality

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Zeros of Bessel J and Y functions

Julia wrapper for the Coinbase Pro API

Solver for complemetarity-based dynamics.

Conversions between Distributions.jl distributions and MeasureTheory.jl measures.

A Julia implementation of estimation and validation algorithms for time series compatible with incomplete data.

A Poker hand evaluator, with Texas hold 'em in mind.

A julia package to implement model selection algorithms on basic regression models.

Tools for creating, moving, and discretizing rigid bodies

A julia package to compute the Distribution of Relaxation Times (DRT) of a given set of impedance measurements.

Variable Selection with Knockoffs

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

Julia package for MPI reconstruction

Fast implementations of FastICA and DUET for blind source separation

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

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