TMB

R package TMB: Template Model Builder: A General Random Effect Tool Inspired by ’ADMB’. With this tool, a user should be able to quickly implement complex random effect models through simple C++ templates. The package combines CppAD (C++ automatic differentiation), Eigen (templated matrix-vector library) and CHOLMOD (sparse matrix routines available from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through BLAS and parallel user templates.


References in zbMATH (referenced in 25 articles , 1 standard article )

Showing results 1 to 20 of 25.
Sorted by year (citations)

1 2 next

  1. Feng, Cindy: Zero-inflated models for adjusting varying exposures: a cautionary note on the pitfalls of using offset (2022)
  2. Helgøy, Ingvild M.; Skaug, Hans J.: The sibling distribution for multivariate life time data (2022)
  3. 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
  4. Kruppa, Jochen; Hothorn, Ludwig: A comparison study on modeling of clustered and overdispersed count data for multiple comparisons (2021)
  5. 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
  6. Monteiro, Andreia; Menezes, Raquel; Silva, Maria Eduarda: Modelling informative time points: an evolutionary process approach (2021)
  7. Zheng, Nan; Cadigan, Noel: Frequentist delta-variance approximations with mixed-effects models and TMB (2021)
  8. Anita K. Nandi, Tim C. D. Lucas, Rohan Arambepola, Peter Gething, Daniel J. Weiss: disaggregation: An R Package for Bayesian Spatial Disaggregation Modelling (2020) arXiv
  9. Cui, Yan; Li, Qi; Zhu, Fukang: Flexible bivariate Poisson integer-valued GARCH model (2020)
  10. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  11. Wood, Simon N.: Inference and computation with generalized additive models and their extensions (2020)
  12. Xu, Xiaofei; Chen, Ying; Chen, Cathy W. S.; Lin, Xiancheng: Adaptive log-linear zero-inflated generalized Poisson autoregressive model with applications to crime counts (2020)
  13. Yan, Yuan; Jeong, Jaehong; Genton, Marc G.: Multivariate transformed Gaussian processes (2020)
  14. Dinsdale, Daniel; Salibian-Barrera, Matias: Modelling Ocean temperatures from bio-probes under preferential sampling (2019)
  15. Flores-Agreda, Daniel; Cantoni, Eva: Bootstrap estimation of uncertainty in prediction for generalized linear mixed models (2019)
  16. Lawler, Ethan; Whoriskey, Kim; Aeberhard, William H.; Field, Chris; Mills Flemming, Joanna: The conditionally autoregressive hidden Markov model (CarHMM): inferring behavioural states from animal tracking data exhibiting conditional autocorrelation (2019)
  17. Selland Kleppe, Tore: Dynamically rescaled Hamiltonian Monte Carlo for Bayesian hierarchical models (2019)
  18. Yin, Yihao; Aeberhard, William H.; Smith, Stephen J.; Flemming, Joanna Mills: Identifiable state-space models: a case study of the Bay of Fundy sea scallop fishery (2019)
  19. Bell, Bradley M.; Kristensen, Kasper: Newton step methods for AD of an objective defined using implicit functions (2018)
  20. Craiu, Radu V.; Duchesne, Thierry: A scalable and efficient covariate selection criterion for mixed effects regression models with unknown random effects structure (2018)

1 2 next