Function reference • PopED

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()`	RUV model: Additive .
`feps.add.prop()`	RUV model: Additive and Proportional.
`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.

Reference

General information

Predefined structural models

Between subject variability models

Predefined residual error models

Create an initial study design and design space

Simulate from the model and design

Evaluate or summarize the design(s)

Optimize a design

Miscellaneous