Toolbox for categorical time-series analysis.

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

Alpha stable and sub-Gaussian distributions in Julia

BitArray representation of genetic sequences in Julia

Nonlinear diffusion problems in Julia

A package that stores Constraint Programming models for JuliaConstraints solvers

Julia wrapper for the Coinbase Pro API

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Utilities to read/write extended GSLIB files in Julia

Use PETSc solvers in sequential and parallel Julia runs

Julia implementation of the Gumbel-Softmax reparametrization trick compatible with Zygote and ForwardDiff

Hypothesis tests of calibration.

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

A Julia package for network decomposition of optimal power flow problems

Linear fractional programming with Julia and JuMP

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

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

Cumulants based features selection and outlier detection

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An extension package of PowerModels.jl for Harmonic (Optimal) Power Network

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

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

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Julia package for MPI reconstruction

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Sampling quantum phase space distributions

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

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Solutions for combinatorial problems using symbolic methods.

Implementation of a drone surveillance problem with POMDPs.jl

A Makie theme

Drawing functions for `SimpleGraphs`

Method to estimate phylogenetic networks from algebraic invariants

Wrapper around the NCBI taxonomy files

Variable Selection with Knockoffs

Power spectra on the masked sky

Graphical Analysis of Structural Causal Models

Dealing with ERA5 Reanalysis datasets

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