Abstract types and interfaces for Markov chain Monte Carlo methods

Adaptive Skip-gram implementation in Julia

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

Robust implementation for random-walk Metropolis-Hastings algorithms

Simulate stochastic timeseries that follow ARFIMA, ARMA, ARIMA, AR, etc. processes

Exploratory analysis of Bayesian models with Julia

⚡ Lightning fast density estimation in Julia ⚡

Basis Function Expansions for Julia

A Bayesian Analysis Toolkit in Julia

Code accompanying the paper Frank van der Meulen, Moritz Schauer: Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals

Bayesian multi-tensor factorization methods, with side information

BayesianNonparametrics in julia

Bayesian Networks for Julia

Bloom filters in Julia

Statistical bootstrapping library for Julia

A statistical toolbox for diffusion processes and stochastic differential equations. Named after the Brownian Bridge.

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

Causal inference, graphical models and structure learning in Julia

Causal inference in non-linear dynamics systems

Cointegration in Vector Error Correction Models in Julia

Compositional data analysis in Julia

A Julia package to support conjugate prior distributions.

System Identification toolbox, compatible with ControlSystems.jl

Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation for Julia.

Continuous time, discrete event system in Julia

Simple least squares and curve fitting functions

Julia module for Detrended Cross-Correlation Analysis.

Statistical bootstrap procedures for time-series data

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

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Dirichlet Process Mixture Models in Julia

Regularized discriminant analysis in Julia.

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

A Julia package for probability distributions and associated functions.

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

A Julia package for evaluation of divergences between distributions

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

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

Online empirical cumulative distribution functions

Extreme value statistics in Julia