ff.PK.1.comp.oral.md.CL()
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Structural model: one-compartment, oral absorption, multiple bolus dose, parameterized using CL. |
ff.PK.1.comp.oral.md.KE()
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Structural model: one-compartment, oral absorption, multiple bolus dose, parameterized using KE. |
ff.PK.1.comp.oral.sd.CL()
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Structural model: one-compartment, oral absorption, single bolus dose, parameterized using CL. |
ff.PK.1.comp.oral.sd.KE()
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Structural model: one-compartment, oral absorption, single bolus dose, parameterized using KE. |
ff.PKPD.1.comp.oral.md.CL.imax()
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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()
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Structural model: one-compartment, single bolus IV dose, parameterized using CL driving an EMAX model with a direct effect. |
evaluate.e.ofv.fim()
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Evaluate the expectation of the Fisher Information Matrix (FIM) and the expectation of the OFV(FIM). |
evaluate.fim()
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Evaluate the Fisher Information Matrix (FIM) |
evaluate_design()
|
Evaluate a design |
evaluate_fim_map()
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Compute the Bayesian Fisher information matrix |
evaluate_power()
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Power of a design to estimate a parameter. |
shrinkage()
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Predict shrinkage of empirical Bayes estimates (EBEs) in a population model |
calc_ofv_and_fim()
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Calculate the Fisher Information Matrix (FIM) and the OFV(FIM) for either point values or parameters or distributions. |
ofv_fim()
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Evaluate a criterion of the Fisher Information Matrix (FIM) |
ofv_criterion()
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Normalize an objective function by the size of the FIM matrix |
get_rse()
|
Compute the expected parameter relative standard errors |
plot_efficiency_of_windows()
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Plot the efficiency of windows |
efficiency()
|
Compute efficiency. |
design_summary()
|
Display a summary of output from poped_db |
poped_optim()
|
Optimize a design defined in a PopED database |
summary(<poped_optim>)
|
Display a summary of output from poped_optim |
optim_ARS()
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Optimize a function using adaptive random search. |
optim_LS()
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Optimize a function using a line search algorithm. |
optimize_groupsize()
|
Title Optimize the proportion of individuals in the design groups |
optimize_n_eff()
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Translate efficiency to number of subjects |
optimize_n_rse()
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Optimize the number of subjects based on desired uncertainty of a parameter. |
LEDoptim()
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Optimization function for D-family, E-family and Laplace approximated ED designs |
RS_opt()
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Optimize the objective function using an adaptive random search algorithm for D-family and E-family designs. |
a_line_search()
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Optimize using line search |
poped_optimize()
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Retired optimization module for PopED |