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

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

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

Statistical bootstrapping library for Julia

Exploratory analysis of Bayesian models with Julia

Probabilistic Circuits from the Juice library

Time series implementation for the Julia language focused on efficiency and flexibility

Large scale Gaussian Mixture Models

Hidden Markov Models for Julia.

A Julia package for manifold learning and nonlinear dimensionality reduction

Frequency tables in Julia

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

Robust implementation for random-walk Metropolis-Hastings algorithms

Abstract types and interfaces for Markov chain Monte Carlo methods

Simple least squares and curve fitting functions

Preheat your MCMC

WIP successor to Soss.jl

Survival analysis in Julia

⚡ Lightning fast density estimation in Julia ⚡

Bayesian Generalized Linear models using `@formula` syntax.

Sequential Monte Carlo algorithm for approximation of posterior distributions.

A package for Gaussian random field generation in Julia

Algorithms for regression analysis (e.g. linear regression and logistic regression)

Compositional data analysis in Julia

Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation for Julia.

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

Some nonnegative least squares solvers in Julia

A Julia package to support conjugate prior distributions.

Julia package containing utilities intended for Time Series analysis.

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E.T. Jaynes home phone.

Reimplementation of Raphaelvallat's Pingouin in Julia

Provides likelihood functions for Gaussian Processes.

Direct and robust methods for outlier detection in linear regression

Fast, LoopVectorization.jl-based summary statistics

Quantile regression in Julia

Statistical Models with Regularization in Pure Julia

The MultipleTesting package offers common algorithms for p-value adjustment and combination and more…

TopicModels for Julia