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.