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 63 articles )

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  1. Papastamoulis, Panagiotis; Ntzoufras, Ioannis: On the identifiability of Bayesian factor analytic models (2022)
  2. Zhang, Anru R.; Cai, T. Tony; Wu, Yihong: Heteroskedastic PCA: algorithm, optimality, and applications (2022)
  3. Alberto Caimo, Lampros Bouranis, Robert Krause, Nial Friel: Statistical Network Analysis with Bergm (2021) arXiv
  4. 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)
  5. Huang, A.; Kim, A. S. I.: Bayesian Conway-Maxwell-Poisson regression models for overdispersed and underdispersed counts (2021)
  6. Perry de Valpine, Sally Paganin, Daniel Turek: compareMCMCs: An R package for studying MCMC efficiency (2021) not zbMATH
  7. Sergio Venturini, Raffaella Piccarreta : A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: The dmbc Package in R (2021) not zbMATH
  8. Ho, Lam Si Tung; Nguyen, Binh T.; Dinh, Vu; Nguyen, Duy: Posterior concentration and fast convergence rates for generalized Bayesian learning (2020)
  9. Moore, Camille M.; Carlson, Nichole E.; MaWhinney, Samantha; Kreidler, Sarah: A Dirichlet process mixture model for non-ignorable dropout (2020)
  10. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  11. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  12. Riko Kelter: bayest: An R Package for Effect-Size Targeted Bayesian Two-Sample t-Tests (2020) not zbMATH
  13. Waleed Almutiry, Vineetha Warriyar K V, Rob Deardon: Continuous Time Individual-Level Models of Infectious Disease: a Package EpiILMCT (2020) arXiv
  14. Armero, Carmen; Cabras, Stefano; Castellanos, María Eugenia; Quirós, Alicia: Two-stage Bayesian approach for GWAS with known genealogy (2019)
  15. da Paz, Rosineide F.; Balakrishnan, Narayanaswamy; Bazán, Jorge Luis: L-logistic regression models: prior sensitivity analysis, robustness to outliers and applications (2019)
  16. George G Vega Yon; Paul Marjoram: fmcmc: A friendly MCMC framework (2019) not zbMATH
  17. Ozanne, Marie V.; Brown, Grant D.; Oleson, Jacob J.; Lima, Iraci D.; Queiroz, Jose W.; Jeronimo, Selma M. B.; Petersen, Christine A.; Wilson, Mary E.: Bayesian compartmental model for an infectious disease with dynamic states of infection (2019)
  18. Shana Scogin; Johannes Karreth; Andreas Beger; Rob Williams: BayesPostEst: An R Package to Generate Postestimation Quantities for Bayesian MCMC Estimation (2019) not zbMATH
  19. Stojkova, Biljana Jonoska; Campbell, David A.: Incremental mixture importance sampling with shotgun optimization (2019)
  20. Bou-Rabee, Nawaf; Sanz-Serna, J. M.: Geometric integrators and the Hamiltonian Monte Carlo method (2018)

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