Julia package for kernel functions for machine learning

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Types and utility functions for summarizing Markov chain Monte Carlo simulations

A Julia interface to the NLopt nonlinear-optimization library

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

[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.

The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

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

Specifying, fitting, and evaluating statistical models in Julia

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

Simple chains

WebGL-based 3D visualizer in Julia

Mathematical functions related to statistics.

Multi-threaded BLAS-like library that provides pure Julia matrix multiplication

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

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

Scalable symbolic-numeric set computations in Julia

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

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

Grid-based simulations in Julia

A Julia interface to the Gurobi Optimizer

Graph Neural Networks in Julia

A Julia wrapper for libpq

Bayesian Networks for Julia

Abstract types and methods for Gaussian Processes.

Financial market technical analysis & indicators in Julia

Algorithms inspired by graph Laplacians: linear equation solvers, sparsification, clustering, optimization, etc.

Tidier data visualization in Julia, modeled after the ggplot2 R package.

A library of data interpolation and smoothing functions

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.