StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

Named dimensions and indexing for julia arrays and other data

Create books with Julia

Julia interface to SymPy via PyCall

Adaptive 1d numerical Gauss–Kronrod integration in Julia

GenX: a configurable power system capacity expansion model for studying low-carbon energy futures. More details at : https://genx.mit.edu

Julia package for kernel functions for machine learning

Julia bindings to Vega-Lite

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Types and utility functions for summarizing Markov chain Monte Carlo simulations

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

A Julia interface to the NLopt nonlinear-optimization library

[DEPRECATED] Bioinformatics and Computational Biology Infrastructure for Julia

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

Julia package for automated Bayesian inference on a factor graph with reactive message passing

:rocket: Julia package for orthogonal polynomial transforms :snowboarder:

Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia

An intuitive modeling interface for infinite-dimensional optimization problems.

Declarative vector graphics

Specifying, fitting, and evaluating statistical models in Julia

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

A Julia/JuMP-based Global Optimization Solver for Non-convex Programs

LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

Fast jacobian computation through sparsity exploitation and matrix coloring

WebGL-based 3D visualizer in Julia

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

Scalable symbolic-numeric set computations in Julia

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

Utility package for accessing common Machine Learning datasets in Julia

Grid-based simulations in Julia

A common interface for quadrature and numerical integration for the SciML scientific machine learning organization

Fast Estimation of Linear Models with IV and High Dimensional Categorical Variables

A Julia interface to the Gurobi Optimizer

Graph Neural Networks in Julia

A Julia package for large-scale tensor computations, with a hint of category theory

Bayesian Networks for Julia

Abstract types and methods for Gaussian Processes.

A Julia wrapper for libpq

Financial market technical analysis & indicators in Julia