• JAGS

  • Referenced in 247 articles [sw08040]
  • program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation ... plaftorm for experimentation with ideas in Bayesian modelling. JAGS is licensed under the GNU General...
  • spBayes

  • Referenced in 365 articles [sw10160]
  • Univariate and Multivariate Hierarchical Point-referenced Spatial Models. Scientists and investigators in such diverse fields ... data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally...
  • tsbridge

  • Referenced in 170 articles [sw12354]
  • tsbridge: Calculate normalising constants for Bayesian time series models. The tsbridge package contains a collection ... variety of time series Bayesian models, where parameters are estimated using BUGS, and models themselves...
  • PMTK

  • Referenced in 211 articles [sw14689]
  • software framework encompassing machine learning, graphical models, and Bayesian statistics (hence the logo). (Some methods...
  • BUGS

  • Referenced in 378 articles [sw07885]
  • flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo...
  • mclust

  • Referenced in 304 articles [sw00563]
  • algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization...
  • DPpackage

  • Referenced in 69 articles [sw10495]
  • DPpackage: Bayesian Semi- and Nonparametric Modeling in R. Data analysis sometimes requires the relaxation ... specification of the probability model. In the Bayesian context, this is accomplished by placing ... implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models...
  • tgp

  • Referenced in 41 articles [sw07921]
  • package tgp: Bayesian treed Gaussian process models. Bayesian nonstationary, semiparametric nonlinear regression and design ... with jumps to the limiting linear model ... Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable...
  • PyMC

  • Referenced in 51 articles [sw10482]
  • PyMC: Bayesian Stochastic Modelling in Python. PyMC is a python module that implements Bayesian statistical...
  • BayesTree

  • Referenced in 64 articles [sw07995]
  • Bayesian Methods for Tree Based Models: Implementation of BART: Bayesian Additive Regression Trees. We develop ... Bayesian “sum-of-trees” model where each tree is constrained by a regularization prior ... inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from ... posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis...
  • Amos

  • Referenced in 60 articles [sw06515]
  • easily compare, confirm and refine models. Uses Bayesian analysis—to improve estimates of model parameters...
  • rjags

  • Referenced in 64 articles [sw08039]
  • package rjags: Bayesian graphical models using MCMC. Interface to the JAGS MCMC library. The rjags ... from R to the JAGS library for Bayesian data analysis. JAGS uses Markov Chain Monte...
  • MrBayes

  • Referenced in 60 articles [sw07715]
  • MrBayes is a program for Bayesian inference and model choice across a wide range...
  • BartPy

  • Referenced in 83 articles [sw40584]
  • regression trees. We develop a Bayesian “sum-of-trees” model where each tree is constrained ... inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from ... posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis ... particular, BART is defined by a statistical model: a prior and a likelihood. This approach...
  • brms

  • Referenced in 31 articles [sw19099]
  • package brms. brms: Bayesian Regression Models using Stan. Fit Bayesian generalized (non-)linear multilevel models ... using Stan for full Bayesian inference. A wide range of distributions and link functions ... zero-inflated, hurdle, and even non-linear models all in a multilevel context. Further modeling...
  • BayesLogit

  • Referenced in 44 articles [sw09312]
  • package BayesLogit: PolyaGamma Sampling. Bayesian inference for logistic models using Pólya-Gamma latent variables ... data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals...
  • bvarsv

  • Referenced in 105 articles [sw11023]
  • bvarsv: Bayesian Analysis of a Vector Autoregressive Model with Stochastic Volatility and Time-Varying Parameters...
  • bayesm

  • Referenced in 66 articles [sw06787]
  • course that emphasizes the value of the Bayesian approach to solving nontrivial problems. The success ... provides efficient implementation of advanced methods and models...
  • ADVI

  • Referenced in 27 articles [sw34040]
  • technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this ... ADVI). The user only provides a Bayesian model and a dataset; nothing else. We make...
  • spTimer

  • Referenced in 18 articles [sw24237]
  • package spTimer: Spatio-Temporal Bayesian Modelling. Fits, spatially predicts and temporally forecasts large amounts ... using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models ... Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar...