ParseItemName

Parse Data Item Names from NONMEM Output

internal

This shows how NONMEM(R) software works. NONMEM's classical
estimation methods like 'First Order(FO) approximation', 'First Order
Conditional Estimation(FOCE)', and 'Laplacian approximation' are explained.
Additionally, provides functions for post-run processing of NONMEM output
files, generating comprehensive PDF diagnostic reports including objective
function value analysis, parameter estimates, prediction diagnostics,
residual diagnostics, empirical Bayes estimate (EBE) analysis, input data
summary, and individual pharmacokinetic parameter distributions.

Kyun-Seop Bae

Understanding Nonlinear Mixed Effects Modeling for Population
Pharmacokinetics

ParseItemName function

<dl><dt>RawRead</dt>
<dd>character vector of lines from NONMEM output</dd></dl>

Arguments

Parse Data Item Names from NONMEM Output — ParseItemName

<dl>

<dt>RawRead</dt>
<dd>character vector of lines from NONMEM output</dd>

</dl>

ParseItemName: Parse Data Item Names from NONMEM Output

Description

Usage

Value

Arguments