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 318 articles )

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  1. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
  2. Alberto Caimo, Lampros Bouranis, Robert Krause, Nial Friel: Statistical Network Analysis with Bergm (2021) arXiv
  3. Bartlett, Thomas E.; Kosmidis, Ioannis; Silva, Ricardo: Two-way sparsity for time-varying networks with applications in genomics (2021)
  4. Bhattarai, Saroj; Chatterjee, Arpita; Park, Woong Yong: Effects of US quantitative easing on emerging market economies (2021)
  5. Bonner, S., Kim, H.-N., Westneat, D., Mutzel, A., Wright, J., Schofield, M.: dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble (2021) not zbMATH
  6. 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)
  7. Corradin, R., Canale, A.,Nipoti, B: BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures (2021) not zbMATH
  8. Elshahhat, Ahmed; Nassar, Mazen: Bayesian survival analysis for adaptive type-II progressive hybrid censored hjorth data (2021)
  9. Erler, N. S., Rizopoulos, D., Lesaffre, E. M. E. H.: JointAI: Joint Analysis and Imputation of Incomplete Data in R (2021) not zbMATH
  10. Ferreira, Marco A. R.; Porter, Erica M.; Franck, Christopher T.: Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects (2021)
  11. Francesco Denti: intRinsic: an R package for model-based estimation of the intrinsic dimension of a dataset (2021) arXiv
  12. Gregor Zens, Sylvia Frühwirth-Schnatter, Helga Wagner: Efficient Bayesian Modeling of Binary and Categorical Data in R: The UPG Package (2021) arXiv
  13. Holbrook, Andrew J.; Loeffler, Charles E.; Flaxman, Seth R.; Suchard, Marc A.: Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data (2021)
  14. Jean-Paul Fox, Konrad Klotzke, Rinke Klein Entink: LNIRT: An R Package for Joint Modeling of Response Accuracy and Times (2021) arXiv
  15. Jeffrey W. Doser, Andrew O. Finley, Marc Kery, Elise F. Zipkin: spOccupancy: An R package for single species, multispecies, and integrated spatial occupancy models (2021) arXiv
  16. Kuschnig, N., Vashold, L.: BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R (2021) not zbMATH
  17. Mayrink, V. D., Duarte, J. D. N., Demarqui, F. N.: pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution (2021) not zbMATH
  18. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  19. Rodney Sparapani, Charles Spanbauer, Robert McCulloch: Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package (2021) not zbMATH
  20. Sergio Venturini, Raffaella Piccarreta : A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: The dmbc Package in R (2021) not zbMATH

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