Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Building and using pyramids for large raster data

Preheat your MCMC

Bayesian inference with probabilistic programming.

EHT Image domain analysis through template matching.

BOSS (Bayesian Optimization with Semiparametric Surrogate)

The Julia implementation of the generalised hierarchical Gaussian filter

Basic Camera Models including pinhole, radial distortion and more.

A Julia package for behavioural modeling

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Ecosystem of numerical methods for microbial kinetics data analysis, from preprocessing to result interpretation.

The base package for Optimization.jl, containing the structs and basic functions for it.

Power spectrum inference of irregularly sampled time series using Gaussian Processes in Julia

Implementation of the multi-affine group and its actions

Composable contracts, models, and functions that allow for modeling of both simple and complex financial instruments

An interface for accelerated simulation of high-dimensional collisionless and electrostatic plasmas.

Lightweight module for fusing physical and neural models

Robust robotic localization and mapping, together with NavAbility(TM). Reach out to info@wherewhen.ai for help.

Bayesian Generalized Linear models using `@formula` syntax.

Useful functions to work with groups defined in Manifolds.jl

Utilities to help testing new groups and actions with Manifolds.jl

Octofitter is a Julia package for performing Bayesian inference against direct images of exoplanets, relative astrometry, and astrometric acceleration of the host star.

Methods for computational information geometry

A library to project an arbitrary function to an exponential family member distribution with the manifold optimization

Differential equations on manifolds

Implementations of the models from the Statistical Rethinking book with Turing.jl

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Bayesian Safety Signal Detection in Julia

High level functions for analyzing the output of simulations

Parameter estimation under uncertainty.

-

Statistical Inference to DEtermine KICKS on compact objects

Optimal experimental design of ODE and DAE systems in julia

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

Julia Package for Active Inference

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

Clique recycling non-Gaussian (multi-modal) factor graph solver; also see Caesar.jl.

Robot Motion Estimate: Tools, Variables, and Factors for SLAM in robotics; also see Caesar.jl.

-