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 25 articles )

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  1. Hughes, John: Sklar’s omega: a Gaussian copula-based framework for assessing agreement (2022)
  2. Liu, Ying; Vats, Dootika; Flegal, James M.: Batch size selection for variance estimators in MCMC (2022)
  3. Jin, Rui; Tan, Aixin: Fast Markov chain Monte Carlo for high-dimensional Bayesian regression models with shrinkage priors (2021)
  4. Michaud, N., de Valpine, P., Turek, D., Paciorek, C. J., Nguyen, D.: Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages (2021) not zbMATH
  5. Zhao, Tingting; Bouchard-Côté, Alexandre: Analysis of high-dimensional continuous time Markov chains using the local bouncy particle sampler (2021)
  6. Kowal, Daniel R.; Canale, Antonio: Simultaneous transformation and rounding (STAR) models for integer-valued data (2020)
  7. Kunkel, Deborah; Peruggia, Mario: Anchored Bayesian Gaussian mixture models (2020)
  8. Park, Joonha; Atchadé, Yves: Markov chain Monte Carlo algorithms with sequential proposals (2020)
  9. Xia, Wei; Doss, Hani: Scalable hyperparameter selection for latent Dirichlet allocation (2020)
  10. Neelon, Brian: Bayesian zero-inflated negative binomial regression based on Pólya-gamma mixtures (2019)
  11. Bezener, Martin; Hughes, John; Jones, Galin: Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging (with discussion) (2018)
  12. Bouchard-Côté, Alexandre; Vollmer, Sebastian J.; Doucet, Arnaud: The bouncy particle sampler: a nonreversible rejection-free Markov chain Monte Carlo method (2018)
  13. George, Clint P.; Doss, Hani: Principled selection of hyperparameters in the latent Dirichlet allocation model (2018)
  14. John Hughes: sklarsomega: An R Package for Measuring Agreement Using Sklar's Omega Coefficient (2018) arXiv
  15. 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)
  16. Zhang, Quan; Zhou, Mingyuan: Permuted and augmented stick-breaking Bayesian multinomial regression (2018)
  17. Zhou, Haiming; Hanson, Timothy: A unified framework for Fitting Bayesian semiparametric models to arbitrarily censored survival data, including spatially referenced data (2018)
  18. Lifeng Lin; Jing Zhang; James Hodges; Haitao Chu: Performing Arm-Based Network Meta-Analysis in R with the pcnetmeta Package (2017) not zbMATH
  19. Liu, Zhuqing; Berrocal, Veronica J.; Bartsch, Andreas J.; Johnson, Timothy D.: Pre-surgical fMRI data analysis using a spatially adaptive conditionally autoregressive model (2016)
  20. Tan, Aixin; Huang, Jian: Bayesian inference for high-dimensional linear regression under mnet priors (2016)

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