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  • WinBUGS
  • Referenced in 556 articles [sw04492]
  • BUGS project, which aims to make practical MCMC methods available to applied statisticians. WinBUGS...

  • GMRFLib
  • Referenced in 222 articles [sw06641]
  • only possible using Markov Chain Monte Carlo (MCMC) techniques. The preeminent experts in the field ... aspects, construct fast and reliable algorithms for MCMC inference, and provide an online C-library...

  • BUGS
  • Referenced in 294 articles [sw07885]
  • statistical models using Markov chain Monte Carlo (MCMC) methods. The project began...

  • CODA
  • Referenced in 190 articles [sw04290]
  • package coda: Output analysis and diagnostics for MCMC , Output analysis and diagnostics for Markov Chain ... output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence...

  • spBayes
  • Referenced in 252 articles [sw10160]
  • involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific...

  • JAGS
  • Referenced in 131 articles [sw08040]
  • hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS...

  • Stan
  • Referenced in 95 articles [sw10200]
  • language implementing full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized maximum likelihood...

  • MLwiN
  • Referenced in 82 articles [sw04837]
  • likelihood estimation and Markov Chain Monte Carlo (MCMC) methods. MLwiN is based on an earlier...

  • OpenBUGS
  • Referenced in 62 articles [sw08316]
  • expert system’, which determines an appropriate MCMC (Markov chain Monte Carlo) scheme (based...

  • Mcmcpack
  • Referenced in 37 articles [sw07974]
  • MCMCpack: Markov chain Monte Carlo (MCMC) Package. This package contains functions to perform Bayesian inference ... Library Version 1.0.3. All models return coda mcmc objects that can then be summarized using...

  • BayesTree
  • Referenced in 50 articles [sw07995]
  • accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively...

  • rjags
  • Referenced in 31 articles [sw08039]
  • package rjags: Bayesian graphical models using MCMC. Interface to the JAGS MCMC library. The rjags ... analysis. JAGS uses Markov Chain Monte Carlo (MCMC) to generate a sequence of dependent samples...

  • SSS
  • Referenced in 24 articles [sw07794]
  • approaches such as Markov chain Monte Carlo (MCMC) methods are often infeasible or ineffective ... from gene expression cancer genomics, comparisons with MCMC and other methods, and theoretical and simulation...

  • MrBayes
  • Referenced in 31 articles [sw07715]
  • models. MrBayes uses Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution...

  • BEAST

  • Referenced in 31 articles [sw12588]
  • BEAST 2 uses Markov chain Monte Carlo (MCMC) to average over tree space, so that...

  • BRugs
  • Referenced in 20 articles [sw08183]
  • BRugs: R interface to the OpenBUGS MCMC software. Fully-interactive R interface to the OpenBUGS ... software for Bayesian analysis using MCMC sampling. Runs natively and stably...

  • MCMCglmm
  • Referenced in 17 articles [sw08302]
  • package MCMCglmm: MCMC Generalised Linear Mixed Models. MCMC Generalised Linear Mixed Models...

  • emcee
  • Referenced in 14 articles [sw20217]
  • emcee: The MCMC Hammer. We introduce a stable, well tested Python implementation of the affine ... ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code ... behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance...

  • Rtwalk
  • Referenced in 16 articles [sw14215]
  • Implementation of the ’t-walk’ MCMC Algorithm. The ’t-walk’ is a general-purpose MCMC...

  • spam
  • Referenced in 22 articles [sw07965]
  • simple structure(s), (3) tailored for MCMC calculations within GMRF. (4) S3 and S4 like...