getfulld

Create a full D (between subject variability) matrix given a vector of variances and covariances.
Note, this does not test matching vector lengths.

internal

Optimal experimental designs for both population and individual
studies based on nonlinear mixed-effect models. Often this is based on a
computation of the Fisher Information Matrix. This package was developed
for pharmacometric problems, and examples and predefined models are available
for these types of systems. The methods are described in Nyberg et al.
(2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004)
<doi:10.1016/S0169-2607(03)00073-7>.

Andrew C. Hooker

PopED

Population (and Individual) Optimal Experimental Design

Marco Foracchia

Eric Stroemberg

Martin Fink

Giulia Lestini

Sebastian Ueckert

Joakim Nyberg

getfulld function

<dl><dt>variance_vector</dt>
<dd>The vector of the variances.</dd>
<dt>covariance_vector</dt>
<dd>A vector of the covariances. Written in column major 
order for the lower triangular matrix.</dd></dl>

Arguments

Create a full D (between subject variability) matrix given a vector of variances and covariances.
Note, this does not test matching vector lengths. — getfulld

<dl>

<dt>variance_vector</dt>
<dd>The vector of the variances.</dd>


<dt>covariance_vector</dt>
<dd>A vector of the covariances. Written in column major 
order for the lower triangular matrix.</dd>

</dl>

getfulld: Create a full D (between subject variability) matrix given a vector of variances and covariances. Note, this does not test matching vector lengths.

Description

Usage

Value

Arguments

Examples