Mcmcpack

MCMCpack: Markov chain Monte Carlo (MCMC) Package. This package contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return coda mcmc objects that can then be summarized using the coda package. MCMCpack also contains some useful utility functions, including some additional density functions and pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization.


References in zbMATH (referenced in 54 articles )

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  1. Alberto Caimo, Lampros Bouranis, Robert Krause, Nial Friel: Statistical Network Analysis with Bergm (2021) arXiv
  2. Chao, Fengqing; Gerland, Patrick; Cook, Alex R.; Alkema, Leontine: Global estimation and scenario-based projections of sex ratio at birth and missing female births using a Bayesian hierarchical time series mixture model (2021)
  3. Sergio Venturini, Raffaella Piccarreta : A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: The dmbc Package in R (2021) not zbMATH
  4. Ho, Lam Si Tung; Nguyen, Binh T.; Dinh, Vu; Nguyen, Duy: Posterior concentration and fast convergence rates for generalized Bayesian learning (2020)
  5. Moore, Camille M.; Carlson, Nichole E.; MaWhinney, Samantha; Kreidler, Sarah: A Dirichlet process mixture model for non-ignorable dropout (2020)
  6. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  7. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  8. Riko Kelter: bayest: An R Package for Effect-Size Targeted Bayesian Two-Sample t-Tests (2020) not zbMATH
  9. Waleed Almutiry, Vineetha Warriyar K V, Rob Deardon: Continuous Time Individual-Level Models of Infectious Disease: a Package EpiILMCT (2020) arXiv
  10. da Paz, Rosineide F.; Balakrishnan, Narayanaswamy; Bazán, Jorge Luis: L-logistic regression models: prior sensitivity analysis, robustness to outliers and applications (2019)
  11. George G Vega Yon; Paul Marjoram: fmcmc: A friendly MCMC framework (2019) not zbMATH
  12. Shana Scogin; Johannes Karreth; Andreas Beger; Rob Williams: BayesPostEst: An R Package to Generate Postestimation Quantities for Bayesian MCMC Estimation (2019) not zbMATH
  13. Bou-Rabee, Nawaf; Sanz-Serna, J. M.: Geometric integrators and the Hamiltonian Monte Carlo method (2018)
  14. Bouranis, Lampros; Friel, Nial; Maire, Florian: Model comparison for Gibbs random fields using noisy reversible jump Markov chain Monte Carlo (2018)
  15. Draxler, Clemens: Bayesian conditional inference for Rasch models (2018)
  16. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH
  17. Ho, Lam Si Tung; Xu, Jason; Crawford, Forrest W.; Minin, Vladimir N.; Suchard, Marc A.: Birth/birth-death processes and their computable transition probabilities with biological applications (2018)
  18. Mair, Patrick: Modern psychometrics with R (2018)
  19. Okada, Kensuke; Mayekawa, Shin-ichi: Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling (2018)
  20. Parisi, Antonio; Liseo, B.: Objective Bayesian analysis for the multivariate skew-(t) model (2018)

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