Probability bounds analysis in Julia

Motif-based weighted spectral clustering in Python, R and Julia

Julia interface to MITgcm

Causal Inference with Invariant Prediction

Geospatial tables compatible with the GeoStats.jl framework

Statically sized tensors and related operations for Julia

A Julian abstraction layer over FreeType.jl

Julia package for fitting mixed-effects models with flexible random/repeated covariance structure.

Models and parameterizations for the turbulent ocean surface boundary layer in Julia

Differential equation problem specifications and scientific machine learning for common financial models

Automates adjoints. Forward and reverse mode algorithmic differentiation around implicit functions (not propagating AD through), as well as custom rules to allow for mixed-mode AD or calling external (non-AD compatible) functions within an AD chain.

Julia package for the plotting of plane curves and surfaces

Naive Bayes classifier

Deep Learning made easy and fast

Powerflow for unbalanced three-phase power distribution systems with distributed generation

A Julia/JuMP Package for Maximizing Algebraic Connectivity of Undirected Weighted Graphs

Accelerate your ML research using pre-built Deep Learning Models with Lux

Provide `sound` & `record` functions for playing & recording audio in Julia. Also Julia version of phase vocoder.

Stochastic delay differential equations (SDDE) solvers for the SciML scientific machine learning ecosystem

Nodes and modes for high order finite element methods

Accurate and fast tight-binding calculations, using pre-fit coefficients and three-body terms.

Specialized tools for MRI

Complex phase portraits for Julia.

@yield-based syntax for iterating over collections—foldl for humans™

Extreme value analysis package for Julia

Sparse matrix class with efficient successive insertion of entries

Functions for declaring and working with grammars and expression trees in Julia.

Express: a high-level, extensible workflow framework for accelerating ab initio calculations for the materials science community

A playground for evolutionary computation in Julia

Builds conductance based neural networks iteratively, from ion channels and synapses

Julia packages for joint optimization of natural gas and power transmission networks

Neural network training, fast and easy.

Reverse-mode automatic differentiation for sparse Hessians

-

-

Basis Function Expansions for Julia

Geothermal Cloud for Machine Learning

Three Dimensional Magnetohydrodynamic(MHD) pseudospectral solvers written in julia with FourierFlows.jl

-

A Julia package for working with spherical harmonic expansions