This repository aims to make available in Julia the functionalities of the Stata command coefplot. Coefplots is built on PGFPlotsX.

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Battery modelling using Jutul.jl and BattMo in MRST

Parse user-agent strings into components

Controllers for airborne wind energy systems

A collection of functions to analyze phonetic data

Julia implementation of the original R sensemakr package: https://github.com/carloscinelli/sensemakr

Julia interface for GeneNetwork REST API

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

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

EBIC - a biclustering algorithm in Julia

Github repo about Taylor diagram implementation in Julia.

Quick and dirty implementation of the Les Houches Event Format (LHE Files) in Julia

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Makie plots with legends and labels and everything

Graphical User Interface for MAGEMin, which runs in your web-browser.

Record linkage in Julia

An Open Nonlinear Structural Analysis Solver in Julia

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Regression models for beta distributed responses in Julia

Tools for working with Brain Imaging Data Structure from Julia

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

Tools for working with ITS_LIVE glacier velocity data

Autologistic regression models in Julia

Role based authorisation (RBA) plugin for Genie.jl

A lightweight Consensus-Based Optimisation package for Julia

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(Hierarchical) Phylogenetic Models in Julia

A Package for Estimating Stochastic Frontier Models using Julia

A Julia package for sampling binomial random variates on an nVidia GPU

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

"yet another paths system"

Prescribed flow models for testing cloud microphysics schemes.

A Julia based implementation of Plug and Play Language Models

Nonlinear diffusion problems in Julia

Implements the Partitioned Least Squares algorithms

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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