704 Open source projects that are alternatives of or similar to Shinystan

rstanarm R package for Bayesian applied regression modeling

Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

RStan, the R interface to Stan

A collection of Bayesian data analysis recipes using PyMC3

Bayesian Data Analysis demos for Python

A common framework for implementing and using log densities for inference.

Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC

Bayesian spatial analysis

Bayesian Data Analysis demos for R

High-performance Bayesian Data Analysis on the GPU in Clojure

Projection predictive variable selection

How to do Bayesian statistical modelling using numpy and PyMC3

CmdStanR: the R interface to CmdStan

bayesplot R package for plotting Bayesian models

loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS)

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

Examples for Bayesian inference using DynamicHMC.jl and related packages.

Statistical Rethinking with PyTorch and Pyro

Full Bayesian Inference for Hidden Markov Models

C++ template library for probabilistic programming

Collection of probabilistic models and inference algorithms

Bayesian inference with probabilistic programming.

The base NIMBLE package for R

Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code

ParaMonte: Plain Powerful Parallel Monte Carlo and MCMC Library for Python, MATLAB, Fortran, C++, C.

brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

A high-level Bayesian analysis API written in Python

Statistical Rethinking course in pymc3

Blang's software development kit

Solutions of practice problems from the Richard McElreath's "Statistical Rethinking" book.

The posterior R package

Bayesian nowcasting with adjustment for delayed and incomplete reporting to estimate COVID-19 infections in the United States

Bayesian Generalized Linear Models with Time-Varying Coefficients

A Bayesian Analysis Toolkit in Julia

Bayesian analysis + tidy data + geoms (R package)

Graphical tools for analyzing Markov Chain Monte Carlo simulations from Bayesian inference

A Python package for building Bayesian models with TensorFlow or PyTorch

GPstuff - Gaussian process models for Bayesian analysis

Multi-core BUGS for fast Bayesian inference of large hierarchical models

Stanマニュアルの日本語への翻訳プロジェクト

BAS R package https://merliseclyde.github.io/BAS/

Bayesian SEIR model to estimate the effects of social-distancing on COVID-19

Tutorial on model assessment, model selection and inference after model selection

Material for AMLD 2020 workshop "Bayesian Inference: embracing uncertainty"

Forecasting 🇫🇷 elections with Bayesian statistics 🥳

library of C++ functions that support applications of Stan in Pharmacometrics

Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.

Helper functions for brmsfit objects (DEPRECATED)

Implementing MCMC sampling from scratch in R for various Bayesian models

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

Statistical Rethinking (2nd Ed) with Tensorflow Probability

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

A python module to detect stellar flares using Bayesian model comparison

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

PyMC3 educational resources

Deep universal probabilistic programming with Python and PyTorch

CmdStan.jl v6 provides an alternative, older Julia wrapper to Stan's `cmdstan` executable.

Expressions and dataframes with statistical details 📉 📜🔣✅

'Visualization in Bayesian workflow' by Gabry, Simpson, Vehtari, Betancourt, and Gelman. (JRSS discussion paper and code)