Probability & Statistics Packages
-
Turing.jl1807Bayesian inference with probabilistic programming.
-
Gen.jl1725A general-purpose probabilistic programming system with programmable inference
-
Distributions.jl987A Julia package for probability distributions and associated functions.
-
OnlineStats.jl762⚡ Single-pass algorithms for statistics
-
POMDPs.jl587MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.
-
GLM.jl538Generalized linear models in Julia
-
StatsBase.jl525Basic statistics for Julia
-
GeoStats.jl414An extensible framework for high-performance geostatistics in Julia
-
Soss.jl401Probabilistic programming via source rewriting
-
Distances.jl376A Julia package for evaluating distances (metrics) between vectors.
-
MixedModels.jl369A Julia package for fitting (statistical) mixed-effects models
-
MeasureTheory.jl367"Distributions" that might not add to one.
-
MultivariateStats.jl344A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
-
Stheno.jl324Probabilistic Programming with Gaussian processes in Julia
-
TimeSeries.jl318Time series toolkit for Julia
-
GaussianProcesses.jl298A Julia package for Gaussian Processes
-
HypothesisTests.jl258Hypothesis tests for Julia
-
Mamba.jl253Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia
-
MonteCarloMeasurements.jl243Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.
-
MCMCChains.jl236Types and utility functions for summarizing Markov chain Monte Carlo simulations
-
MCMCChain.jl236Types and utility functions for summarizing Markov chain Monte Carlo simulations
-
DynamicHMC.jl231Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
-
BayesNets.jl215Bayesian Networks for Julia
-
Stan.jl197Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.
-
FixedEffectModels.jl197Fast Estimation of Linear Models with IV and High Dimensional Categorical Variables
-
AdvancedHMC.jl192Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms
-
LowRankModels.jl185LowRankModels.jl is a julia package for modeling and fitting generalized low rank models.
-
AdaGram.jl169Adaptive Skip-gram implementation in Julia
-
BAT.jl164A Bayesian Analysis Toolkit in Julia
-
Omega.jl155Causal, Higher-Order, Probabilistic Programming
-
TuringModels.jl153Implementations of the models from the Statistical Rethinking book with Turing.jl
-
KernelDensity.jl150Kernel density estimators for Julia
-
DistributionsAD.jl142Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff
-
CausalInference.jl134Causal inference, graphical models and structure learning with the PC algorithm.
-
Lasso.jl133Lasso/Elastic Net linear and generalized linear models
-
StatsKit.jl131Convenience meta-package to load essential packages for statistics
-
DiffEqBayes.jl117Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
-
DynamicPPL.jl116Implementation of domain-specific language (DSL) for dynamic probabilistic programming
-
TSAnalysis.jl113A Julia implementation of basic tools for time series analysis compatible with incomplete data.
-
MessyTimeSeries.jl113A Julia implementation of basic tools for time series analysis compatible with incomplete data.
Loading more...