Package index • PopED

General information

PopED PopED-package: 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.