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 high-performance geostatistics in Julia

Probabilistic programming via source rewriting

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

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)

Probabilistic Programming with Gaussian processes in Julia

Time series toolkit for Julia

A Julia package for Gaussian Processes

Hypothesis tests for Julia

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

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

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Types and utility functions for summarizing Markov chain Monte Carlo simulations

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

Bayesian Networks for Julia

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

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

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

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

Adaptive Skip-gram implementation in Julia

A Bayesian Analysis Toolkit in Julia

Causal, Higher-Order, Probabilistic Programming

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

Kernel density estimators for Julia

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

Causal inference, graphical models and structure learning with the PC algorithm.

Lasso/Elastic Net linear and generalized linear models

Convenience meta-package to load essential packages for statistics

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

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

A Julia implementation of basic tools for time series analysis compatible with incomplete data.

A Julia implementation of basic tools for time series analysis compatible with incomplete data.