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 15 articles , 1 standard article )

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  1. 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
  2. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  3. Wood, Simon N.: Inference and computation with generalized additive models and their extensions (2020)
  4. Yan, Yuan; Jeong, Jaehong; Genton, Marc G.: Multivariate transformed Gaussian processes (2020)
  5. Dinsdale, Daniel; Salibian-Barrera, Matias: Modelling Ocean temperatures from bio-probes under preferential sampling (2019)
  6. Flores-Agreda, Daniel; Cantoni, Eva: Bootstrap estimation of uncertainty in prediction for generalized linear mixed models (2019)
  7. 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)
  8. 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)
  9. Bell, Bradley M.; Kristensen, Kasper: Newton step methods for AD of an objective defined using implicit functions (2018)
  10. Craiu, Radu V.; Duchesne, Thierry: A scalable and efficient covariate selection criterion for mixed effects regression models with unknown random effects structure (2018)
  11. Selland Kleppe, Tore: Modified Cholesky Riemann manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets (2018)
  12. Sofie Pødenphant, Kasper Kristensen, Per B. Brockhoff: The Multiplicative Mixed Model with the mumm R package as a General and Easy Random Interaction Model Tool (2018) arXiv
  13. Niku, Jenni; Warton, David I.; Hui, Francis K. C.; Taskinen, Sara: Generalized linear latent variable models for multivariate count and biomass data in ecology (2017)
  14. Kasper Kristensen and Anders Nielsen and Casper Berg and Hans Skaug and Bradley Bell: TMB: Automatic Differentiation and Laplace Approximation (2016) not zbMATH
  15. Perry de Valpine; Daniel Turek; Christopher J. Paciorek; Clifford Anderson-Bergman; Duncan Temple Lang; Rastislav Bodik: Programming with models: writing statistical algorithms for general model structures with NIMBLE (2015) arXiv