nimble

R package nimble: Flexible BUGS-Compatible System for Hierarchical Statistical Modeling and Algorithm Development. Flexible application of algorithms to models specified in the BUGS language. Algorithms can be written in the NIMBLE language and made available to any model.


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Ma, Zhihua; Chen, Guanghui: Bayesian semiparametric latent variable model with DP prior for joint analysis: implementation with nimble (2020)
  2. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  3. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  4. Finke, Axel; King, Ruth; Beskos, Alexandros; Dellaportas, Petros: Efficient sequential Monte Carlo algorithms for integrated population models (2019)
  5. Maleki, Mohsen; Wraith, Darren: Mixtures of multivariate restricted skew-normal factor analyzer models in a Bayesian framework (2019)
  6. Maleki, Mohsen; Wraith, Darren; Arellano-Valle, Reinaldo B.: A flexible class of parametric distributions for Bayesian linear mixed models (2019)
  7. Risser, Mark D.; Paciorek, Christopher J.; Stone, Dáithí A.: Spatially dependent multiple testing under model misspecification, with application to detection of anthropogenic influence on extreme climate events (2019)
  8. Stoner, Oliver; Economou, Theo; Drummond Marques da Silva, Gabriela: A hierarchical framework for correcting under-reporting in count data (2019)
  9. Nicholas Michaud, Perry de Valpine, Daniel Turek, Christopher J. Paciorek: Sequential Monte Carlo Methods in the nimble R Package (2017) arXiv
  10. Turek, Daniel; de Valpine, Perry; Paciorek, Christopher J.; Anderson-Bergman, Clifford: Automated parameter blocking for efficient Markov chain Monte Carlo sampling (2017)