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 23 articles )

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  1. 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)
  2. Brunero Liseo, Antonio Parisi: Objective Bayesian analysis for the multivariate skew-t model (2017) arXiv
  3. Dries Benoit and Dirk Van den Poel: bayesQR: A Bayesian Approach to Quantile Regression (2017)
  4. Härdle, Karl Wolfgang; Okhrin, Ostap; Okhrin, Yarema: Basic elements of computational statistics (2017)
  5. Mauricio Sarrias and Ricardo Daziano: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package (2017)
  6. Nalan Baştürk and Stefano Grassi and Lennart Hoogerheide and Anne Opschoor and Herman van Dijk: The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference (2017)
  7. Bayerstadler, Andreas; van Dijk, Linda; Winter, Fabian: Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance (2016)
  8. Leung, Dennis; Drton, Mathias: Order-invariant prior specification in Bayesian factor analysis (2016)
  9. Xavier Fernández-i-Marín: ggmcmc: Analysis of MCMC Samples and Bayesian Inference (2016)
  10. Khandoker Bakar; Sujit Sahu: spTimer: Spatio-Temporal Bayesian Modeling Using R (2015)
  11. Macaro, Christian; Prado, Raquel: Spectral decompositions of multiple time series: a Bayesian non-parametric approach (2014)
  12. Scherer, Ralph; Schaarschmidt, Frank; Prescher, Sabine; Priesnitz, Kai U.: Simultaneous confidence intervals for comparing biodiversity indices estimated from overdispersed count data (2013)
  13. Aston, John A.D.; Peng, J.Y.; Martin, Donald E.K.: Implied distributions in multiple change point problems (2012)
  14. Ji, Yonggang; Lin, Nan; Zhang, Baoxue: Model selection in binary and Tobit quantile regression using the Gibbs sampler (2012)
  15. Kaplan, David; Chen, Jianshen: A two-step Bayesian approach for propensity score analysis: simulations and case study (2012)
  16. Klein, Martin; Neerchal, Nagaraj; Sinha, Bimal; Chiu, Weihsueh; White, Paul: Statistical inferences from serially correlated methylene chloride data (2012)
  17. R. Chalmers: mirt: A Multidimensional Item Response Theory Package for the R Environment (2012)
  18. Macaro, Christian: Bayesian non-parametric signal extraction for Gaussian time series (2010)
  19. Nott, David J.; Leng, Chenlei: Bayesian projection approaches to variable selection in generalized linear models (2010)
  20. Raftery, Adrian E.; Bao, Le: Estimating and projecting trends in HIV/AIDS generalized epidemics using incremental mixture importance sampling (2010)

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