Bayesian inference with probabilistic programming.

A general-purpose probabilistic programming system with programmable inference

A Julia package for probability distributions and associated functions.

⚡ Single-pass algorithms for statistics

MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.

Generalized linear models in Julia

Basic statistics for Julia

An extensible framework for geospatial data science and geostatistical modeling fully written in Julia

A Julia package for evaluating distances (metrics) between vectors.

Probabilistic programming via source rewriting

A Julia package for fitting (statistical) mixed-effects models

"Distributions" that might not add to one.

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

Time series toolkit for Julia

Probabilistic Programming with Gaussian processes in Julia

A Julia package for Gaussian Processes

Hypothesis tests for Julia

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

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

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

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

Bayesian Networks for Julia

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

A Bayesian Analysis Toolkit in Julia

LowRankModels.jl is a julia package for modeling and fitting generalized low rank models.

Causal inference, graphical models and structure learning in Julia

Kernel density estimators for Julia

Adaptive Skip-gram implementation in Julia

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

Causal, Higher-Order, Probabilistic Programming

Implementation of domain-specific language (DSL) for dynamic probabilistic programming

Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff

Lasso/Elastic Net linear and generalized linear models

Convenience meta-package to load essential packages for statistics

System Identification toolbox, compatible with ControlSystems.jl

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