runSymbolicAnalysis

Aggressively tries to reduce fill-in of sparse Cholesky factor by
running a full suite of ordering algorithms. NOTE: requires a
specialized installation of the package. More information is
available at the package URL.

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.

Kasper Kristensen

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

Brad Bell

Hans Skaug

Arni Magnusson

Casper Berg

Anders Nielsen

Martin Maechler

Theo Michelot

Mollie Brooks

Alex Forrence

Christoffer Moesgaard Albertsen

Cole Monnahan

runSymbolicAnalysis function

<dl><dt>obj</dt>
<dd>Output from <code>MakeADFun</code></dd></dl>

Arguments

Run symbolic analysis on sparse Hessian — runSymbolicAnalysis

<dl>

<dt>obj</dt>
<dd>Output from <code>MakeADFun</code></dd>

</dl>

Run symbolic analysis on sparse Hessian

runSymbolicAnalysis: Run symbolic analysis on sparse Hessian

Description

Usage

Arguments