RTMB course at International Statistical Ecology Conference - Sunday, 14 July
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Mollie Brooks
Apr 2, 2024, 7:30:04 AMApr 2
to TMB Users, us...@admb-project.org
Dear TMB and ADMB users lists,
RTMB: Automatic differentiation in R
Date: Sunday 14 July
Length: Full day
Instructors: Anders Nielsen (DTU – Danish Technical University, Denmark); Ben Bolker (University of McMaster, Canada)
Overview: This workshop will introduce participants to RTMB. RTMB is the latest iteration of TMB (Template Model Builder), a widely used tool for flexible statistical modeling. RTMB’s novel feature is that models are specified in plain R code (rather than C++ code or another special modelling language). Once the model is specified, it evaluates function values and derivatives as efficiently as if the model were written in C++. Calculations in RTMB are supported by automatic differentiation, sparse matrices, delta-method calculations, and efficient Laplace approximation, which makes RTMB ideal for maximum likelihood inference in small and large models with and without random effects. Models specified in RTMB can also sampled with MCMC by linking them to Stan. In the course, examples and exercises will span non-linear regression, generalized linear models, random effects, state-space, and spatio-temporal models with flexible spatial mesh structure (as known from INLA).
cheers,
Mollie
Sorry for crossposting.
I would like your help letting people know about a course being offered in person at ISEC in Swansea, Wales on the 14th of July. The workshop fee is 50 GBP. Registration and more information is here https://statisticalecology.org/?page_id=58
RTMB: Automatic differentiation in R
Date: Sunday 14 July
Length: Full day
Instructors: Anders Nielsen (DTU – Danish Technical University, Denmark); Ben Bolker (University of McMaster, Canada)
Overview: This workshop will introduce participants to RTMB. RTMB is the latest iteration of TMB (Template Model Builder), a widely used tool for flexible statistical modeling. RTMB’s novel feature is that models are specified in plain R code (rather than C++ code or another special modelling language). Once the model is specified, it evaluates function values and derivatives as efficiently as if the model were written in C++. Calculations in RTMB are supported by automatic differentiation, sparse matrices, delta-method calculations, and efficient Laplace approximation, which makes RTMB ideal for maximum likelihood inference in small and large models with and without random effects. Models specified in RTMB can also sampled with MCMC by linking them to Stan. In the course, examples and exercises will span non-linear regression, generalized linear models, random effects, state-space, and spatio-temporal models with flexible spatial mesh structure (as known from INLA).
cheers,
Mollie
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