"Distributions" that might not add to one.

A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)

Probabilistic Programming with Gaussian processes in Julia

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

Surrogate modeling and optimization for scientific machine learning (SciML)

Modeling and simulation of multidomain engineering systems

Simple curve fitting in Julia

A Julia package for Gaussian Processes

Hypothesis tests for Julia

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

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Types and utility functions for summarizing Markov chain Monte Carlo simulations

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

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

An intuitive modeling interface for infinite-dimensional optimization problems.

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

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

Bayesian Networks for Julia

Abstract types and methods for Gaussian Processes.

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

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

Reservoir computing utilities for scientific machine learning (SciML)

Abstractions, algorithms, and utilities for reinforcement learning in Julia

Implementation of normalising flows and constrained random variable transformations

Hyperparameter optimization in Julia.

Research package for automatic differentiation of programs containing discrete randomness.

A Bayesian Analysis Toolkit in Julia

Clapeyron provides a framework for the development and use of fluid-thermodynamic models, including SAFT, cubic, activity, multi-parameter, and COSMO-SAC.

Solution of nonlinear multiphysics partial differential equation systems using the Voronoi finite volume method

Causal inference, graphical models and structure learning in Julia

Tools for the exploration of chaos and nonlinear dynamics

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

A Julia package for solving systems of polynomials via homotopy continuation.

A package for binary and continuous, single and multi-material, truss and continuum, 2D and 3D topology optimization on unstructured meshes using automatic differentiation in Julia.

Kernel density estimators for Julia

Boosted trees in Julia

Fast, general-purpose interactive applications for complex systems

Julia hybrid constraint programming solver enhanced by a reinforcement learning driven search.