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TMB (version 1.7.14)

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

Description

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.

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Version

Install

install.packages('TMB')

Monthly Downloads

39,735

Version

1.7.14

License

GPL-2

Maintainer

Kasper Kristensen

Last Published

June 23rd, 2018

Functions in TMB (1.7.14)

summary.checkConsistency

Summarize output from checkConsistency
newton

Generalized newton optimizer.
template

Create cpp template to get started.
openmp

Control number of openmp threads.
tmbprofile

Adaptive likelihood profiling.
newtonOption

Set newton options for a model object.
tmbroot

Compute likelihood profile confidence intervals of a TMB object by root-finding
summary.sdreport

summary tables of model parameters
runSymbolicAnalysis

Run symbolic analysis on sparse Hessian
sdreport

General sdreport function.
as.list.sdreport

Convert estimates to original list format.
gdbsource

Source R-script through gdb to get backtrace.
confint.tmbprofile

Profile based confidence intervals.
MakeADFun

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

Benchmark parallel templates
compile

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

Create minimal R-code corresponding to a cpp template.
print.checkConsistency

Print output from checkConsistency
precompile

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

Check consistency and Laplace accuracy
config

Get or set internal configuration variables
dynlib

Add dynlib extension
plot.tmbprofile

Plot likelihood profile.
normalize

Normalize process likelihood using the Laplace approximation.
print.sdreport

Print brief model summary
runExample

Run one of the test examples.
oneStepPredict

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