AD Model Builder

AD Model Builder This software package should be of interest to anyone who wants to create nonlinear statistical models to analyze data. It is very fast!. If you find Statistical packages such as S-Plus or Matlab are too slow you owe it to yourself to check this out! Free download of evaluation version for MINGW32 C++ or Visual C++ version 5.0 and 6.0 or Borland C++ version 5.5. Runs on Redhat Linux 5.2 and 6.x too.

References in zbMATH (referenced in 17 articles )

Showing results 1 to 17 of 17.
Sorted by year (citations)

  1. Mohamed, Shakir; Rosca, Mihaela; Figurnov, Michael; Mnih, Andriy: Monte Carlo gradient estimation in machine learning (2020)
  2. Olsen, Christian Haargaard; Ottesen, Johnny T.; Smith, Ralph C.; Olufsen, Mette S.: Parameter subset selection techniques for problems in mathematical biology (2019)
  3. Patterson, Toby A.; Parton, Alison; Langrock, Roland; Blackwell, Paul G.; Thomas, Len; King, Ruth: Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges (2017)
  4. Rafael Moral; John Hinde; Clarice Demétrio: Half-Normal Plots and Overdispersed Models in R: The hnp Package (2017) not zbMATH
  5. Wang, S.; Cadigan, N. G.; Benoît, H. P.: Inference about regression parameters using highly stratified survey count data with over-dispersion and repeated measurements (2017)
  6. Asar, Özgür; Ritchie, James; Kalra, Philip A.; Diggle, Peter J.: Short-term and long-term effects of acute kidney injury in chronic kidney disease patients: a longitudinal analysis (2016)
  7. Huang, Lu; Tang, Li; Zhang, Bo; Zhang, Zhiwei; Zhang, Hui: Comparison of different computational implementations on fitting generalized linear mixed-effects models for repeated count measures (2016)
  8. Kasper Kristensen and Anders Nielsen and Casper Berg and Hans Skaug and Bradley Bell: TMB: Automatic Differentiation and Laplace Approximation (2016) not zbMATH
  9. Ricardo Oliveros-Ramos, Yunne-Jai Shin: Calibrar: an R package for fitting complex ecological models (2016) arXiv
  10. 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
  11. Xu, Ximing; Cantoni, Eva; Flemming, Joanna Mills; Field, Chris: Robust state space models for estimating fish stock maturities (2015)
  12. Michael Braun: trustOptim: An R Package for Trust Region Optimization with Sparse Hessians (2014) not zbMATH
  13. Skaug, Hans J.; Yu, Jun: A flexible and automated likelihood based framework for inference in stochastic volatility models (2014)
  14. Vale, R. T. R.; Fewster, R. M.; Carroll, E. L.; Patenaude, N. J.: Maximum likelihood estimation for model (M_t,\alpha) for capture-recapture data with misidentification (2014)
  15. Cattelan, Manuela; Varin, Cristiano: Hybrid pairwise likelihood analysis of animal behavior experiments (2013)
  16. Fournier, David A.; Skaug, Hans J.; Ancheta, Johnoel; Ianelli, James; Magnusson, Arni; Maunder, Mark N.; Nielsen, Anders; Sibert, John: AD model builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models (2012)
  17. Picchini, Umberto; Ditlevsen, Susanne: Practical estimation of high dimensional stochastic differential mixed-effects models (2011)