Template Model Builder: A General Random Effect Tool Inspired by 'ADMB'
Convert estimates to original list format.
Benchmark parallel templates
Check consistency and Laplace accuracy
Compile a C++ template to DLL suitable for MakeADFun.
Get or set internal configuration variables
Profile based confidence intervals.
Add dynlib extension
Free memory allocated on the C++ side by MakeADFun.
Source R-script through gdb to get backtrace.
Gauss Kronrod configuration
Construct objective functions with derivatives based on a compiled C++...
Generalized newton optimizer.
Set newton options for a model object.
Normalize process likelihood using the Laplace approximation.
Calculate one-step-ahead (OSA) residuals for a latent variable model.
Control number of OpenMP threads used by a TMB model.
Plot likelihood profile.
Precompile the TMB library in order to speed up compilation of templat...
Print output from checkConsistency
Print brief model summary
Create minimal R-code corresponding to a cpp template.
Run one of the test examples.
Run symbolic analysis on sparse Hessian
General sdreport function.
Sequential reduction configuration
Summarize output from checkConsistency
summary tables of model parameters
Create cpp template to get started.
Version information on API and ABI.
Adaptive likelihood profiling.
Compute likelihood profile confidence intervals of a TMB object by roo...
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.