CODA

R package coda: Output analysis and diagnostics for MCMC , Output analysis and diagnostics for Markov Chain Monte Carlo simulations. Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 220 articles )

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  1. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  2. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  3. Andersson, C.; Rajala, T.; Särkkä, A.: A Bayesian hierarchical point process model for epidermal nerve fiber patterns (2019)
  4. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle: An R Package for Spatio-Temporal Change of Support (2019) arXiv
  5. Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus: Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP (2019) arXiv
  6. Arellano-Valle, Reinaldo B.; Contreras-Reyes, Javier E.; Quintero, Freddy O. López; Valdebenito, Abel: A skew-normal dynamic linear model and Bayesian forecasting (2019)
  7. Bao, Yiqi; Cancho, Vicente G.; Louzada, Francisco; Suzuki, Adriano K.: Semi-parametric cure rate proportional odds models with spatial frailties for interval-censored data (2019)
  8. Bitto, Angela; Frühwirth-Schnatter, Sylvia: Achieving shrinkage in a time-varying parameter model framework (2019)
  9. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  10. Golightly, Andrew; Bradley, Emma; Lowe, Tom; Gillespie, Colin S.: Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models (2019)
  11. Gregg, Robert W.; Sarkar, Saumendra N.; Shoemaker, Jason E.: Mathematical modeling of the cGAS pathway reveals robustness of DNA sensing to TREX1 feedback (2019)
  12. Gronau, Quentin F.; Wagenmakers, Eric-Jan; Heck, Daniel W.; Matzke, Dora: A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling (2019)
  13. Guan, Yawen; Sampson, Christian; Tucker, J. Derek; Chang, Won; Mondal, Anirban; Haran, Murali; Sulsky, Deborah: Computer model calibration based on image warping metrics: an application for sea ice deformation (2019)
  14. Heck, Daniel W.; Overstall, Antony M.; Gronau, Quentin F.; Wagenmakers, Eric-Jan: Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models (2019)
  15. Hystad, Grethe; Eleish, Ahmed; Hazen, Robert M.; Morrison, Shaunna M.; Downs, Robert T.: Bayesian estimation of Earth’s undiscovered mineralogical diversity using noninformative priors (2019)
  16. João Duarte; Vinícius Mayrink: slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis (2019) not zbMATH
  17. Johnson, Margaret; Caragea, Petruţa C.; Meiring, Wendy; Jeganathan, C.; Atkinson, Peter M.: Bayesian dynamic linear models for estimation of phenological events from remote sensing data (2019)
  18. Lewis-Beck, Colin; Zhu, Zhengyuan; Mondal, Anirban; Song, Joon Jin; Hobbs, Jonathan; Hornbuckle, Brian; Patton, Jason: A parametric approach to unmixing remote sensing crop growth signatures (2019)
  19. Malcolm S. Itter, Jarno Vanhatalo, Andrew O. Finley: EcoMem: An R package for quantifying ecological memory (2019) arXiv
  20. Neelon, Brian: Bayesian zero-inflated negative binomial regression based on Pólya-gamma mixtures (2019)

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