## General information

PopED

PopED - Population (and individual) optimal Experimental Design.

## Predefined structural models

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

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

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

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

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

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

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

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

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

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

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 effect.

## Between subject variability models

build_sfg()

Build PopED parameter function from a model function

## Predefined residual error models

Define a residual unexplained variability (RUV) model.

feps.add()

feps.add.prop()

feps.prop()

RUV model: Proportional.

## Create an initial study design and design space

Create an initial study design, and design space, if optimizing. Input the design and model information into a format that PopED understands.

create.poped.database()

Create a PopED database

create_design()

Create design variables for a full description of a design.

create_design_space()

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

## Simulate from the model and design

plot_model_prediction()

Plot model predictions

model_prediction()

Model predictions

## Evaluate or summarize the design(s)

evaluate.e.ofv.fim()

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

evaluate.fim()

Evaluate the Fisher Information Matrix (FIM)

evaluate_design()

Evaluate a design

evaluate_fim_map()

Compute the Bayesian Fisher information matrix

evaluate_power()

Power of a design to estimate a parameter.

shrinkage()

Predict shrinkage of empirical Bayes estimates (EBEs) in a population model

calc_ofv_and_fim()

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

ofv_fim()

Evaluate a criterion of the Fisher Information Matrix (FIM)

ofv_criterion()

Normalize an objective function by the size of the FIM matrix

get_rse()

Compute the expected parameter relative standard errors

plot_efficiency_of_windows()

Plot the efficiency of windows

efficiency()

Compute efficiency.

design_summary()

Display a summary of output from poped_db

## Optimize a design

Optimize a design given a model, design and design space.

poped_optim()

Optimize a design defined in a PopED database

summary(<poped_optim>)

Display a summary of output from poped_optim

optim_ARS()

Optimize a function using adaptive random search.

optim_LS()

Optimize a function using a line search algorithm.

optimize_groupsize()

Title Optimize the proportion of individuals in the design groups

optimize_n_eff()

Translate efficiency to number of subjects

optimize_n_rse()

Optimize the number of subjects based on desired uncertainty of a parameter.

LEDoptim()

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

RS_opt()

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

a_line_search()

Optimize using line search

poped_optimize()

Retired optimization module for PopED

## Miscellaneous

cell()

Create a cell array (a matrix of lists)

mc_mean()

Compute the monte-carlo mean of a function

median_hilow_poped()

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

ones()

Create a matrix of ones

pargen()

Parameter simulation

poped_gui()

Run the graphical interface for PopED

size()

Function written to match MATLAB's size function

start_parallel()

Start parallel computational processes

tic()

Timer function (as in MATLAB)

toc()

Timer function (as in MATLAB)

zeros()

Create a matrix of zeros.