Pareto smoothed importance sampling

Preheat your MCMC

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

Bayesian inference with probabilistic programming.

A package for Gaussian random field generation in Julia

Causal inference, graphical models and structure learning in Julia

Time series toolkit for Julia

Exploratory analysis of Bayesian models with Julia

Hypothesis tests for Julia

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

Julia package for automated Bayesian inference on a factor graph with reactive message passing

Direct and robust methods for outlier detection in linear regression

Abstract types and interfaces for Markov chain Monte Carlo methods

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

Basic statistics for Julia

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

Archive of functions that emulate R's d-p-q-r functions for probability distributions

Training images for geostastical simulation

Compositional data analysis in Julia

A Julia package for probability distributions and associated functions.

⚡ Single-pass algorithms for statistics

Utilities to read/write extended GSLIB files in Julia

Extreme value statistics in Julia

Large scale Gaussian Mixture Models

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

"Distributions" that might not add to one.

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

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Bayesian Generalized Linear models using `@formula` syntax.

TopicModels for Julia

A general-purpose probabilistic programming system with programmable inference

System Identification toolbox, compatible with ControlSystems.jl

Probabilistic Programming with Gaussian processes in Julia

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

Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html

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

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

BridgeStan provides efficient in-memory access through Python, Julia, and R to the methods of a Stan model.

Julia package for fitting mixed-effects models with flexible random/repeated covariance structure.