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

Misc julia code that have not yet found its home in a package...

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Extreme value statistics in Julia

Online empirical cumulative distribution functions

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

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

A Julia package for evaluation of divergences between distributions

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

A Julia package for probability distributions and associated functions.

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

Regularized discriminant analysis in Julia.

Dirichlet Process Mixture Models in Julia

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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

Statistical bootstrap procedures for time-series data

Julia module for Detrended Cross-Correlation Analysis.

Simple least squares and curve fitting functions

Continuous time, discrete event system in Julia

Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation for Julia.

System Identification toolbox for LTI systems, compatible with ControlSystems.jl

A Julia package to support conjugate prior distributions.

Compositional data analysis in Julia

Cointegration in Vector Error Correction Models in Julia

Causal inference in non-linear dynamics systems

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

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

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

Statistical bootstrapping library for Julia

Bloom filters in Julia

Bayesian Networks for Julia

BayesianNonparametrics in julia

Bayesian multi-tensor factorization methods, with side information

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

A Bayesian Analysis Toolkit in Julia

Basis Function Expansions for Julia

⚡ Lightning fast density estimation in Julia ⚡

Exploratory analysis of Bayesian models with Julia

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