R package mcmcse: Monte Carlo Standard Errors for MCMC. mcmcse provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE computation for expectation and quantile estimators is supported. The package also provides functions for computing effective sample size and for plotting Monte Carlo estimates versus sample size.

References in zbMATH (referenced in 17 articles )

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  1. Kowal, Daniel R.; Canale, Antonio: Simultaneous transformation and rounding (STAR) models for integer-valued data (2020)
  2. Neelon, Brian: Bayesian zero-inflated negative binomial regression based on Pólya-gamma mixtures (2019)
  3. Bezener, Martin; Hughes, John; Jones, Galin: Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging (with discussion) (2018)
  4. Bouchard-Côté, Alexandre; Vollmer, Sebastian J.; Doucet, Arnaud: The bouncy particle sampler: a nonreversible rejection-free Markov chain Monte Carlo method (2018)
  5. George, Clint P.; Doss, Hani: Principled selection of hyperparameters in the latent Dirichlet allocation model (2018)
  6. John Hughes: sklarsomega: An R Package for Measuring Agreement Using Sklar's Omega Coefficient (2018) arXiv
  7. Link, William A.; Converse, Sarah J.; Yackel Adams, Amy A.; Hostetter, Nathan J.: Analysis of population change and movement using robust design removal data (2018)
  8. Zhang, Quan; Zhou, Mingyuan: Permuted and augmented stick-breaking Bayesian multinomial regression (2018)
  9. Zhou, Haiming; Hanson, Timothy: A unified framework for Fitting Bayesian semiparametric models to arbitrarily censored survival data, including spatially referenced data (2018)
  10. Lifeng Lin; Jing Zhang; James Hodges; Haitao Chu: Performing Arm-Based Network Meta-Analysis in R with the pcnetmeta Package (2017) not zbMATH
  11. Liu, Zhuqing; Berrocal, Veronica J.; Bartsch, Andreas J.; Johnson, Timothy D.: Pre-surgical fMRI data analysis using a spatially adaptive conditionally autoregressive model (2016)
  12. Tan, Aixin; Huang, Jian: Bayesian inference for high-dimensional linear regression under mnet priors (2016)
  13. Craiu, Radu V.; Gray, Lawrence; Łatuszyński, Krzysztof; Madras, Neal; Roberts, Gareth O.; Rosenthal, Jeffrey S.: Stability of adversarial Markov chains, with an application to adaptive MCMC algorithms (2015)
  14. Ghosh, Joyee; Tan, Aixin: Sandwich algorithms for Bayesian variable selection (2015)
  15. Johnson, Alicia A.; Jones, Galin L.: Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models (2015)
  16. Doss, Charles R.; Flegal, James M.; Jones, Galin L.; Neath, Ronald C.: Markov chain Monte Carlo estimation of quantiles (2014)
  17. Roy, Vivekananda: Convergence rates for MCMC algorithms for a robust Bayesian binary regression model (2012)