Package: PopED 0.6.0.9005

Andrew C. Hooker

PopED: Population (and Individual) Optimal Experimental Design

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. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) <doi:10.1016/S0169-2607(03)00073-7>.

Authors:Andrew C. Hooker [aut, cre, trl, cph], Marco Foracchia [aut], Eric Stroemberg [ctb], Martin Fink [ctb], Giulia Lestini [ctb], Sebastian Ueckert [aut], Joakim Nyberg [aut]

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PopED.pdf |PopED.html✨
PopED/json (API)
NEWS

# Install 'PopED' in R:
install.packages('PopED', repos = c('https://andrewhooker.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/andrewhooker/poped/issues

On CRAN:

nlmeoptimal-designpharmacodynamicspharmacokineticspharmacometricspkpdpopulationpopulation-model

80 exports 31 stars 3.48 score 38 dependencies 1 dependents 8 mentions 314 scripts 438 downloads

Last updated 6 days agofrom:ee8f50b966. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 12 2024
R-4.5-winNOTESep 12 2024
R-4.5-linuxNOTESep 12 2024
R-4.4-winOKSep 12 2024
R-4.4-macOKSep 12 2024
R-4.3-winOKSep 12 2024
R-4.3-macOKSep 12 2024

Exports:a_line_searchbfgsb_minblockexpblockfinalblockheaderblockoptbuild_sfgcalc_autofocuscalc_ofv_and_fimcalc_ofv_and_gradcellconvert_variablescreate_designcreate_design_spacecreate.poped.databasedesign_summarydiag_matlabDoptimDtraceed_laplace_ofved_mftotefficiencyevaluate_designevaluate_fim_mapevaluate_powerevaluate.e.ofv.fimevaluate.fimfeps.addfeps.add.propfeps.propfevalff.PK.1.comp.oral.md.CLff.PK.1.comp.oral.md.KEff.PK.1.comp.oral.sd.CLff.PK.1.comp.oral.sd.KEff.PKPD.1.comp.oral.md.CL.imaxff.PKPD.1.comp.sd.CL.emaxfilepartsget_all_paramsget_rseget_unfixed_paramsgetfulldgetTruncatedNormalgradf_epsinvisemptyLEDoptimLinMatrixLLinMatrixL_occLinMatrixLHmc_meanmedian_hilow_popedmfeamftotmodel_predictionofv_criterionofv_fimonesoptim_ARSoptim_LSoptimize_groupsizeoptimize_n_effoptimize_n_rsepargenplot_efficiency_of_windowsplot_model_predictionpoped_guipoped_optimpoped_optimizepoped.chooserandrandnRS_optshrinkagesizestart_paralleltest_mat_sizetictoczeros

Dependencies:bootclicodetoolscolorspacedplyrfansifarvergenericsggplot2gluegtablegtoolsisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellmvtnormnlmepillarpkgconfigpurrrR6RColorBrewerrlangscalesstringistringrtibbletidyselectutf8vctrsviridisLitewithr

Examples

Giulia Lestini, Martin Fink and Andrew Hooker

Rendered fromexamples.Rmdusingknitr::rmarkdownon Sep 12 2024.

Last update: 2023-11-16
Started: 2018-09-03

Introduction to PopED

Andrew Hooker

Rendered fromintro-poped.Rmdusingknitr::rmarkdownon Sep 12 2024.

Last update: 2023-11-16
Started: 2016-08-29

Readme and manuals

Help Manual

Help pageTopics
Optimize using line searcha_line_search
Build PopED parameter function from a model functionbuild_sfg
Calculate the Fisher Information Matrix (FIM) and the OFV(FIM) for either point values or parameters or distributions.calc_ofv_and_fim
Create a cell array (a matrix of lists)cell
Create design variables for a full description of a design.create_design
Create design variables and a design space for a full description of an optimization problem.create_design_space
Create a PopED databasecreate.poped.database
Display a summary of output from poped_dbdesign_summary
Compute efficiency.efficiency
Evaluate a designevaluate_design
Compute the Bayesian Fisher information matrixevaluate_fim_map
Power of a design to estimate a parameter.evaluate_power
Evaluate the expectation of the Fisher Information Matrix (FIM) and the expectation of the OFV(FIM).evaluate.e.ofv.fim
Evaluate the Fisher Information Matrix (FIM)evaluate.fim
RUV model: Additive .feps.add
RUV model: Additive and Proportional.feps.add.prop
RUV model: Proportional.feps.prop
Structural model: one-compartment, oral absorption, multiple bolus dose, parameterized using CL.ff.PK.1.comp.oral.md.CL
Structural model: one-compartment, oral absorption, multiple bolus dose, parameterized using KE.ff.PK.1.comp.oral.md.KE
Structural model: one-compartment, oral absorption, single bolus dose, parameterized using CL.ff.PK.1.comp.oral.sd.CL
Structural model: one-compartment, oral absorption, single bolus dose, parameterized using KE.ff.PK.1.comp.oral.sd.KE
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.oral.md.CL.imax
Structural model: one-compartment, single bolus IV dose, parameterized using CL driving an EMAX model with a direct effect.ff.PKPD.1.comp.sd.CL.emax
Compute the expected parameter relative standard errorsget_rse
Optimization function for D-family, E-family and Laplace approximated ED designsLEDoptim
Compute the monte-carlo mean of a functionmc_mean
Wrap summary functions from Hmisc and ggplot to work with stat_summary in ggplotmedian_hilow_poped
Model predictionsmodel_prediction
Normalize an objective function by the size of the FIM matrixofv_criterion
Evaluate a criterion of the Fisher Information Matrix (FIM)ofv_fim
Create a matrix of onesones
Optimize a function using adaptive random search.optim_ARS
Optimize a function using a line search algorithm.optim_LS
Title Optimize the proportion of individuals in the design groupsoptimize_groupsize
Translate efficiency to number of subjectsoptimize_n_eff
Optimize the number of subjects based on desired uncertainty of a parameter.optimize_n_rse
Parameter simulationpargen
Plot the efficiency of windowsplot_efficiency_of_windows
Plot model predictionsplot_model_prediction
Run the graphical interface for PopEDpoped_gui
Optimize a design defined in a PopED databasepoped_optim
Retired optimization module for PopEDpoped_optimize
Optimize the objective function using an adaptive random search algorithm for D-family and E-family designs.RS_opt
Predict shrinkage of empirical Bayes estimates (EBEs) in a population modelshrinkage
Function written to match MATLAB's size functionsize
Start parallel computational processesstart_parallel
Display a summary of output from poped_optimsummary.poped_optim
Timer function (as in MATLAB)tic
Timer function (as in MATLAB)toc
Create a matrix of zeros.zeros