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PopED: Population (and individual) Experimental Design in R

PopED computes 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 (FIM).

Installation

You need to have R installed. Download the latest version of R from www.r-project.org. Install PopED in R using one of the following methods:

latest stable release -- From CRAN. Write at the R command line:

install.packages("PopED")

Latest development version -- from Github. Note that the command below installs the "master"

(development) branch; if you want the release branch from Github add ref="release" to the install_github() call. The install_github() approach requires that you build from source, i.e. make and compilers must be installed on your system -- see the R FAQ for your operating system; you may also need to install dependencies manually.

devtools::install_github("andrewhooker/PopED")

Getting started

To get started you need to define

A model.
An initial design (and design space if you want to optimize).
The tasks to perform.

There are a number of functions to help you with these tasks. See ?poped for more information.

There are several other examples, as r-scripts, in the "examples" folder in the PopED installation directory located at:

system.file("examples", package="PopED")

The same examples are located in the "inst/examples" directory of this repository.

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Version

Install

install.packages('PopED')

Monthly Downloads

606

Version

0.3.2

License

LGPL (>= 3)

Maintainer

Andrew C Hooker

Last Published

December 12th, 2016

Functions in PopED (0.3.2)

Nonlinear minimization using BFGS with box constraints

Result function for optimization routines

Compute the autofocus portion of the stochastic gradient routine

Optimize using line search

Summarize your optimization settings for optimization routines

Summarize your experiment for optimization routines

Header function for optimization routines

Create a cell array (a matrix of lists)

calc_ofv_and_grad

Compute an objective function and gradient

calc_ofv_and_fim

Calculate the Fisher Information Matrix (FIM) and the OFV(FIM) for either point values or parameters or distributions.

Trace optimization routines

Evaluate the expectation of determinant the Fisher Information Matrix (FIM) using the Laplace approximation.

Evaluate the expectation of the Fisher Information Matrix (FIM) and the expectation of the OFV(FIM).

Create design variables for a full description of a design.

create_design_space

Create design variables and a design space for a full decription of an optimization problem.

Function written to match MATLAB's diag function

D-family optimization function

create.poped.database

Create a PopED database

downsizing_general_design

Downsize a general design to a specific design

convert_variables

Create global variables in the PopED database

Compute efficiency.

evaluate.e.ofv.fim

Evaluate the expectation of the Fisher Information Matrix (FIM) and the expectation of the OFV(FIM).

MATLAB feval function

Evaluate the Fisher Information Matrix (FIM)

RUV model: Additive .

ff.PK.1.comp.oral.md.CL

Structural model: one-compartment, oral absorption, multiple bolus dose, parameterized using CL.

RUV model: Additive and Proportional.

evaluate_design

Evaluate a design

RUV model: Proportional.

ff.PK.1.comp.oral.md.KE

Structural model: one-compartment, oral absorption, multiple bolus dose, parameterized using KE.

Compute the expected parameter relative standard errors

ff.PKPD.1.comp.oral.md.CL.imax

Structural model: one-compartment, oral absoprtion, multiple bolus dose, parameterized using CL driving an inhibitory IMAX model with a direct efect.

ff.PK.1.comp.oral.sd.KE

Structural model: one-compartment, oral absorption, single bolus dose, parameterized using KE.

ff.PK.1.comp.oral.sd.CL

Structural model: one-compartment, oral absorption, single bolus dose, parameterized using CL.

Create a full D (between subject variability) matrix given a vector of variances and covariances.

ff.PKPD.1.comp.sd.CL.emax

Structural model: one-compartment, single bolus IV dose, parameterized using CL driving an EMAX model with a direct efect.

MATLAB fileparts function

get_unfixed_params

Return all the unfixed parameters

Extract all model parameters from the PopED database.

getTruncatedNormal

Generate a random sample from a truncated normal distribution.

Model linearization with respect to epsilon and eta.

Model linearization with respect to epsilon.

Model linearization with respect to occasion variablity parameters.

Compute the inverse of a matrix

Function written to match MATLAB's isempty function

Optimization function for D-family, E-family and Laplace approximated ED designs

Function written to match MATLAB's isfield function

Model linearization with respect to epsilon.

The linearized matrix L

Compute the natural log of the PDF for the parameters in an E-family design

Modified Federov Exchange Algorithm

The full Fisher Information Matrix (FIM) for one individual parameterized with A,B,C matrices & using the derivative of variance.

The reduced Fisher Information Matrix (FIM) for one individual, using the SD of RUV as a parameter.

Compute the monte-carlo mean of a function

The full Fisher Information Matrix (FIM) for one individual Calculating one model switch at a time, good for large matrices.

median_hilow_poped

Wrap summary functions from Hmisc and ggplot to work with stat_summary in ggplot

The reduced Fisher Information Matrix (FIM) for one individual parameterized with A,B,C matrices & using the derivative of variance.

The reduced Fisher Information Matrix (FIM) for one individual

The full Fisher Information Matrix (FIM) for one individual

Evaluate the Fisher Information Matrix (FIM)

The Fisher Information Matrix (FIM) using weighted models

Normalize an objective function by the size of the FIM matrix

model_prediction

Model predictions

The full Fisher Information Matrix (FIM)

The reduced Fisher Information Matrix (FIM)

The full Fisher Information Matrix (FIM) Calculating one model switch at a time, good for large matrices.

The full Fisher Information Matrix (FIM) parameterized with A,B,C matrices & using the derivative of variance.

The reduced Fisher Information Matrix (FIM) using the SD of RUV as a parameter.

The Fisher Information Matrix (FIM) some other method

The reduced Fisher Information Matrix (FIM) parameterized with A,B,C matrices & using the derivative of variance.

Creates a matrix of ones

Parameter simulation

plot_model_prediction

Plot model predictions

Evaluate a criterion of the Fisher Information Matrix (FIM)

Run the graphical interface for PopED

Optimization Using a Line Search Algorithm.

plot_efficiency_of_windows

Plot the efficiency of windows

Optimization Using Adaptive Random Search.

Optimization main module for PopED

Optimization main module for PopED

Optimize the objective function using an adaptive random search algorithm for D-family designs.

Optimize the objective function using an adaptive random search algorithm for D-family and E-family designs.

Function written to match MATLAB's rand function

Function written to match MATLAB's randperm function

Function written to match MATLAB's randn function

Start parallel computational processes

summary.poped_optim

Display a summary of output from poped_optim

Choose between arg1 and arg2

PopED - Population (and individual) optimal Experimental Design.

Function written to match MATLAB's size function

Timer function (as in MATLAB)

Timer function (as in MATLAB)

Creates a matrix of zeros.

Test if any matrix element is zero.

Test if any matrix element is above a minimum value.

Test if any matrix element is above a max value.

Test to make sure that matricies are the right size