TMB1.9.15 package

Template Model Builder: A General Random Effect Tool Inspired by 'ADMB'

as.list.sdreport

Convert estimates to original list format.

benchmark

Benchmark parallel templates

checkConsistency

Check consistency and Laplace accuracy

compile

Compile a C++ template to DLL suitable for MakeADFun.

config

Get or set internal configuration variables

confint.tmbprofile

Profile based confidence intervals.

dynlib

Add dynlib extension

FreeADFun

Free memory allocated on the C++ side by MakeADFun.

gdbsource

Source R-script through gdb to get backtrace.

GK

Gauss Kronrod configuration

MakeADFun

Construct objective functions with derivatives based on a compiled C++...

newton

Generalized newton optimizer.

newtonOption

Set newton options for a model object.

normalize

Normalize process likelihood using the Laplace approximation.

oneStepPredict

Calculate one-step-ahead (OSA) residuals for a latent variable model.

openmp

Control number of OpenMP threads used by a TMB model.

plot.tmbprofile

Plot likelihood profile.

precompile

Precompile the TMB library in order to speed up compilation of templat...

print.checkConsistency

Print output from checkConsistency

print.sdreport

Print brief model summary

Rinterface

Create minimal R-code corresponding to a cpp template.

runExample

Run one of the test examples.

runSymbolicAnalysis

Run symbolic analysis on sparse Hessian

sdreport

General sdreport function.

SR

Sequential reduction configuration

summary.checkConsistency

Summarize output from checkConsistency

summary.sdreport

summary tables of model parameters

template

Create cpp template to get started.

TMB.Version

Version information on API and ABI.

tmbprofile

Adaptive likelihood profiling.

tmbroot

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.

  • Maintainer: Kasper Kristensen
  • License: GPL-2
  • Last published: 2024-09-09