Type:	Package
Title:	Population Fisher Information Matrix
Version:	7.0
Date:	2025-07-02
Maintainer:	Romain Leroux <romainlerouxPFIM@gmail.com>
NeedsCompilation:	no
Description:	Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>, Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>, Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>, Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>, Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.
URL:	http://www.pfim.biostat.fr/, https://github.com/packagePFIM
BugReports:	https://github.com/packagePFIM/PFIM/issues
Depends:	R (≥ 4.0.0)
License:	GPL (≥ 3)
Encoding:	UTF-8
VignetteBuilder:	knitr
Imports:	utils, inline, Deriv, methods, deSolve, purrr, stringr, S7, Matrix, ggplot2, Rcpp, RcppArmadillo, pracma, kableExtra, tibble, scales, knitr
Collate:	'Administration.R' 'AdministrationConstraints.R' 'Fim.R' 'PFIMProject.R' 'Optimization.R' 'PGBOAlgorithm.R' 'PSOAlgorithm.R' 'SimplexAlgorithm.R' 'FedorovWynnAlgorithm.R' 'MultiplicativeAlgorithm.R' 'Model.R' 'Arm.R' 'BayesianFim.R' 'ModelError.R' 'Combined1.R' 'Constant.R' 'Design.R' 'Distribution.R' 'Evaluation.R' 'IndividualFim.R' 'LibraryOfModels.R' 'LibraryOfPDModels.R' 'LibraryOfPKModels.R' 'LogNormal.R' 'ModelODE.R' 'ModelAnalytic.R' 'ModelInfusion.R' 'ModelAnalyticInfusion.R' 'ModelAnalyticInfusionSteadyState.R' 'ModelAnalyticSteadyState.R' 'ModelODEBolus.R' 'ModelODEDoseInEquations.R' 'ModelODEDoseNotInEquations.R' 'ModelODEInfusion.R' 'ModelODEInfusionDoseInEquation.R' 'ModelParameter.R' 'Normal.R' 'PFIM-package.R' 'PopulationFim.R' 'Proportional.R' 'SamplingTimeConstraints.R' 'SamplingTimes.R' 'zzz.R'
RoxygenNote:	7.3.2
Suggests:	rmarkdown, testthat (≥ 3.0.0)
Packaged:	2025-07-02 10:19:20 UTC; MrLer
Author:	Romain Leroux [aut, cre], France Mentré [aut], Jérémy Seurat [ctb]
Repository:	CRAN
Date/Publication:	2025-07-02 12:10:05 UTC

Fisher Information matrix for design evaluation/optimization for nonlinear mixed effects models.

Description

Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) doi:10.1093/biomet/84.2.429, Retout S, Comets E, Samson A, Mentré F (2007) doi:10.1002/sim.2910, Bazzoli C, Retout S, Mentré F (2009) doi:10.1002/sim.3573, Le Nagard H, Chao L, Tenaillon O (2011) doi:10.1186/1471-2148-11-326, Combes FP, Retout S, Frey N, Mentré F (2013) doi:10.1007/s11095-013-1079-3 and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) doi:10.1016/j.cmpb.2021.106126.

Description

Nonlinear mixed effects models (NLMEM) are widely used in model-based drug development and use to analyze longitudinal data. The use of the "population" Fisher Information Matrix (FIM) is a good alternative to clinical trial simulation to optimize the design of these studies. The present version, **PFIM 7.0**, is an R package that uses the S4 object system for evaluating and/or optimizing population designs based on FIM in NLMEMs.

This version of **PFIM** now includes a library of models implemented also using the object oriented system S4 of R. This library contains two libraries of pharmacokinetic (PK) and/or pharmacodynamic (PD) models. The PK library includes model with different administration routes (bolus, infusion, first-order absorption), different number of compartments (from 1 to 3), and different types of eliminations (linear or Michaelis-Menten). The PD model library, contains direct immediate models (e.g. Emax and Imax) with various baseline models, and turnover response models. The PK/PD models are obtained with combination of the models from the PK and PD model libraries. **PFIM** handles both analytical and ODE models and offers the possibility to the user to define his/her own model(s). In **PFIM 7.0**, the FIM is evaluated by first order linearization of the model assuming a block diagonal FIM as in Mentré et al. (1997). The Bayesian FIM is also available to give shrinkage predictions (Combes et al., 2013). **PFIM 7.0** includes several algorithms to conduct design optimization based on the D-criterion, given design constraints: the simplex algorithm (Nelder-Mead) (Nelder & Mead, 1965), the multiplicative algorithm (Seurat et al., 2021), the Fedorov-Wynn algorithm (Fedorov, 1972), PSO (*Particle Swarm Optimization*) and PGBO (*Population Genetics Based Optimizer*) (Le Nagard et al., 2011).

Documentation

Documentation and user guide are available at http://www.pfim.biostat.fr/

Validation

**PFIM 7.0** also provides quality control with tests and validation using the evaluated FIM to assess the validity of the new version and its new features. Finally, **PFIM 7.0** displays all the results with both clear graphical form and a data summary, while ensuring their easy manipulation in R. The standard data visualization package ggplot2 for R is used to display all the results with clear graphical form (Wickham, 2016). A quality control using the D-criterion is also provided.

Organization of the source files in the '/R' folder

**PFIM 7.0** contains a hierarchy of S4 classes with corresponding methods and functions serving as constructors. All of the source code related to the specification of a certain class is contained in a file named '[Name_of_the_class]-Class.R'. These classes include:

1. all roxygen '@include' to insure the correctly generated collate for the DESCRIPTION file, 2. a description of purpose and slots of the class, 3. specification of an initialize method, 4. all getter and setter, respectively returning attributes of the object and associated objects.

Author(s)

Maintainer: Romain Leroux romainlerouxPFIM@gmail.com (ORCID)

Authors:

• France Mentré france.mentre@inserm.fr (ORCID)

Other contributors:

• Jérémy Seurat jeremy.seurat@inserm.fr (ORCID) [contributor]

References

Dumont C, Lestini G, Le Nagard H, Mentré F, Comets E, Nguyen TT, et al. PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models. Comput Methods Programs Biomed. 2018;156:217-29.

Chambers JM. Object-Oriented Programming, Functional Programming and R. Stat Sci. 2014;29:167-80.

Mentré F, Mallet A, Baccar D. Optimal Design in Random-Effects Regression Models. Biometrika. 1997;84:429-42.

Combes FP, Retout S, Frey N, Mentré F. Prediction of shrinkage of individual parameters using the Bayesian information matrix in nonlinear mixed effect models with evaluation in pharmacokinetics. Pharm Res. 2013;30:2355-67.

Nelder JA, Mead R. A simplex method for function minimization. Comput J. 1965;7:308-13.

Seurat J, Tang Y, Mentré F, Nguyen, TT. Finding optimal design in nonlinear mixed effect models using multiplicative algorithms. Computer Methods and Programs in Biomedicine, 2021.

Fedorov VV. Theory of Optimal Experiments. Academic Press, New York, 1972.

Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. Proc. of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, 4-6 October 1995, 39-43.

Le Nagard H, Chao L, Tenaillon O. The emergence of complexity and restricted pleiotropy in adapting networks. BMC Evol Biol. 2011;11:326.

Wickham H. ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New York, 2016.

See Also

Useful links:

• http://www.pfim.biostat.fr/

• https://github.com/packagePFIM

• Report bugs at https://github.com/packagePFIM/PFIM/issues

Administration

Description

The class Administration represents the administration and stores information concerning the administration for the dosage regimen.

Usage

Administration(
  outcome = character(0),
  timeDose = numeric(0),
  dose = numeric(0),
  Tinf = numeric(0),
  tau = 0
)

Arguments

AdministrationConstraints

Description

The class AdministrationConstraints represents the constraint of an input to the system. The class stores information concerning the constraints for the dosage regimen.

Usage

AdministrationConstraints(outcome = character(0), doses = list())

Arguments

Arm

Description

The class Arm represents an arm and stores information concerning an arm.

Usage

Arm(
  name = character(0),
  size = numeric(0),
  administrations = list(),
  initialConditions = list(),
  samplingTimes = list(),
  administrationsConstraints = list(),
  samplingTimesConstraints = list(),
  evaluationModel = list(),
  evaluationGradients = list(),
  evaluationVariance = list(),
  evaluationFim = Fim()
)

Arguments

BayesianFim

Description

The class BayesianFim represents and stores information for the Bayesian Fim.

Usage

BayesianFim(
  fisherMatrix = numeric(0),
  fixedEffects = numeric(0),
  varianceEffects = numeric(0),
  SEAndRSE = list(),
  condNumberFixedEffects = 0,
  condNumberVarianceEffects = 0,
  shrinkage = numeric(0)
)

Arguments

Combined1

Description

The class Combined1 represents and stores information for the error model Combined1.

Usage

Combined1(
  output = character(0),
  equation = expression(sigmaInter + sigmaSlope * output),
  derivatives = list(),
  sigmaInter = 0,
  sigmaSlope = 0,
  sigmaInterFixed = FALSE,
  sigmaSlopeFixed = FALSE,
  cError = 1
)

Arguments

Constant

Description

The class Constant represents and stores information for the error model Constant.

Usage

Constant(
  output = character(0),
  equation = expression(sigmaInter),
  derivatives = list(),
  sigmaInter = 0,
  sigmaSlope = 0,
  sigmaInterFixed = FALSE,
  sigmaSlopeFixed = FALSE,
  cError = 1
)

Arguments

Dcriterion: get the D-criterion of the Fim.

Description

Dcriterion: get the D-criterion of the Fim.

Arguments

Value

A double giving the D-criterion of the Fim.

Design

Description

The class Design represents and stores information for the Design.

Usage

Design(
  name = character(0),
  size = 0,
  arms = list(),
  evaluationArms = list(),
  numberOfArms = 0,
  fim = Fim()
)

Arguments

Details

Design

Distribution

Description

The class Distribution represents and stores information for the parameter distribution.

Usage

Distribution(name = character(0), mu = 0, omega = 0)

Arguments

Evaluation

Description

The class Evaluation represents and stores information for the evaluation of a design

Usage

Evaluation(
  evaluationDesign = list(),
  name = character(0),
  modelParameters = list(),
  modelEquations = list(),
  modelFromLibrary = list(),
  modelError = list(),
  designs = list(),
  outputs = list(),
  fimType = character(0),
  odeSolverParameters = list()
)

Arguments

FedorovWynnAlgorithm

Description

The class FedorovWynnAlgorithm implements the FedorovWynn algorithm.

Usage

FedorovWynnAlgorithm(
  name = character(0),
  modelEquations = list(),
  modelFromLibrary = list(),
  modelParameters = list(),
  modelError = list(),
  optimizer = character(0),
  optimizerParameters = list(),
  outputs = list(),
  designs = list(),
  fimType = character(0),
  fim = Fim(),
  odeSolverParameters = list(),
  optimisationDesign = list(),
  optimisationAlgorithmOutputs = list(),
  elementaryProtocols = list(),
  numberOfSubjects = 0,
  proportionsOfSubjects = 0,
  showProcess = FALSE,
  FedorovWynnAlgorithmOutputs = list()
)

Arguments

Fedorov-Wynn algorithm in Rcpp.

Description

Run the FedorovWynnAlgorithm in Rcpp

Usage

FedorovWynnAlgorithm_Rcpp(
  protocols_input,
  ndimen_input,
  nbprot_input,
  numprot_input,
  freq_input,
  nbdata_input,
  vectps_input,
  fisher_input,
  nok_input,
  protdep_input,
  freqdep_input
)

Arguments

Value

A list giving the results of the outputs of the FedorovWynn algorithm.

Fim

Description

The class Fim represents and stores information for the Fim.

Usage

Fim(
  fisherMatrix = numeric(0),
  fixedEffects = numeric(0),
  varianceEffects = numeric(0),
  SEAndRSE = list(),
  condNumberFixedEffects = 0,
  condNumberVarianceEffects = 0,
  shrinkage = numeric(0)
)

Arguments

IndividualFim

Description

The class IndividualFim represents and stores information for the IndividualFim.

Usage

IndividualFim(
  fisherMatrix = numeric(0),
  fixedEffects = numeric(0),
  varianceEffects = numeric(0),
  SEAndRSE = list(),
  condNumberFixedEffects = 0,
  condNumberVarianceEffects = 0,
  shrinkage = numeric(0)
)

Arguments

LibraryOfModels

Description

The class LibraryOfModels represents and stores information for the LibraryOfModels.

Usage

LibraryOfModels(models = list())

Arguments

LibraryOfPDModels

Description

The class LibraryOfPDModels represents and stores information for the LibraryOfPDModels.

Usage

LibraryOfPDModels

Format

An object of class PFIM::LibraryOfPDModels (inherits from PFIM::LibraryOfModels, S7_object) of length 1.

LibraryOfPKModels

Description

The class LibraryOfPKModels represents and stores information for the LibraryOfPKModels.

Usage

LibraryOfPKModels

Format

An object of class PFIM::LibraryOfPKModels (inherits from PFIM::LibraryOfModels, S7_object) of length 1.

Model Linear2BolusSingleDose_ClQV1V2

Description

Model Linear2BolusSingleDose_ClQV1V2

Usage

Linear2BolusSingleDose_ClQV1V2()

Model Linear2BolusSingleDose_kk12k21V

Description

Model Linear2BolusSingleDose_kk12k21V

Usage

Linear2BolusSingleDose_kk12k21V()

Model Linear2BolusSteadyState_ClQV1V2tau

Description

Model Linear2BolusSteadyState_ClQV1V2tau

Usage

Linear2BolusSteadyState_ClQV1V2tau()

Model Linear2BolusSteadyState_kk12k21Vtau

Description

Model Linear2BolusSteadyState_kk12k21Vtau

Usage

Linear2BolusSteadyState_kk12k21Vtau()

Model Linear2FirstOrderSingleDose_kaClQV1V2

Description

Model Linear2FirstOrderSingleDose_kaClQV1V2

Usage

Linear2FirstOrderSingleDose_kaClQV1V2()

Model Linear2FirstOrderSingleDose_kakk12k21V

Description

Model Linear2FirstOrderSingleDose_kakk12k21V

Usage

Linear2FirstOrderSingleDose_kakk12k21V()

Model Linear2FirstOrderSteadyState_kaClQV1V2tau

Description

Model Linear2FirstOrderSteadyState_kaClQV1V2tau

Usage

Linear2FirstOrderSteadyState_kaClQV1V2tau()

Model Linear2FirstOrderSteadyState_kakk12k21Vtau

Description

Model Linear2FirstOrderSteadyState_kakk12k21Vtau

Usage

Linear2FirstOrderSteadyState_kakk12k21Vtau()

Model Linear2InfusionSingleDose_ClQV1V2

Description

Model Linear2InfusionSingleDose_ClQV1V2

Usage

Linear2InfusionSingleDose_ClQV1V2()

Model Linear2InfusionSingleDose_kk12k21V

Description

Model Linear2InfusionSingleDose_kk12k21V

Usage

Linear2InfusionSingleDose_kk12k21V()

Model Linear2InfusionSteadyState_ClQV1V2tau

Description

Model Linear2InfusionSteadyState_ClQV1V2tau

Usage

Linear2InfusionSteadyState_ClQV1V2tau()

Model Linear2InfusionSteadyState_kk12k21Vtau

Description

Model Linear2InfusionSteadyState_kk12k21Vtau

Usage

Linear2InfusionSteadyState_kk12k21Vtau()

LogNormal

Description

The class LogNormal implements the LogNormal distribution.

Usage

LogNormal(name = character(0), mu = 0, omega = 0)

Arguments

Model

Description

The class Model represents and stores information for a model.

Usage

Model(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list()
)

Arguments

ModelAnalytic

Description

The class ModelAnalytic is used to defined an analytic model.

Usage

ModelAnalytic(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  wrapperModelAnalytic = list(),
  functionArgumentsModelAnalytic = list(),
  functionArgumentsSymbolModelAnalytic = list(),
  solverInputs = list()
)

Arguments

ModelAnalyticInfusion

Description

The class ModelAnalyticInfusion is used to defined an analytic model in infusion.

Usage

ModelAnalyticInfusion(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  wrapperModelAnalyticInfusion = list(),
  functionArgumentsModelAnalyticInfusion = list(),
  functionArgumentsSymbolModelAnalyticInfusion = list(),
  solverInputs = list()
)

Arguments

ModelAnalyticInfusionSteadyState

Description

The class ModelAnalyticInfusionSteadyState is used to defined an analytic model in infusion steady state.

Usage

ModelAnalyticInfusionSteadyState(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  wrapperModelAnalyticInfusion = list(),
  functionArgumentsModelAnalyticInfusion = list(),
  functionArgumentsSymbolModelAnalyticInfusion = list(),
  solverInputs = list()
)

Arguments

Details

ModelAnalyticInfusionSteadyState

ModelAnalyticSteadyState

Description

The class ModelAnalyticSteadyState is used to defined an analytic model in steady state.

Usage

ModelAnalyticSteadyState(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  wrapperModelAnalytic = list(),
  functionArgumentsModelAnalytic = list(),
  functionArgumentsSymbolModelAnalytic = list(),
  solverInputs = list()
)

Arguments

Details

ModelAnalyticSteadyState

ModelError

Description

The class ModelError is used to defined a model error.

Usage

ModelError(
  output = "output",
  equation = expression(),
  derivatives = list(),
  sigmaInter = 0.1,
  sigmaSlope = 0,
  sigmaInterFixed = FALSE,
  sigmaSlopeFixed = FALSE,
  cError = 1
)

Arguments

Details

ModelError

ModelInfusion

Description

The class ModelInfusion is used to defined a model in infusion.

Usage

ModelInfusion(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list()
)

Arguments

ModelODE

Description

The class ModelODE is used to defined a ode model.

Usage

ModelODE(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list()
)

Arguments

ModelODEBolus

Description

The class ModelODEBolus is used to defined a model ode admin bolus.

Usage

ModelODEBolus(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  modelODE = function() NULL,
  doseEvent = list(),
  solverInputs = list()
)

Arguments

ModelODEDoseNotInEquations

Description

The class ModelODEDoseNotInEquations is used to defined a ModelODEDoseNotInEquations

Usage

ModelODEDoseInEquations(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  modelODEDoseInEquations = function() NULL,
  solverInputs = list()
)

Arguments

ModelODEDoseNotInEquations

Description

The class ModelODEDoseNotInEquations is used to defined a ModelODEDoseNotInEquations

Usage

ModelODEDoseNotInEquations(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  modelODE = function() NULL,
  doseEvent = list(),
  solverInputs = list()
)

Arguments

ModelODEInfusion

Description

The class ModelODEInfusion is used to defined a model ModelODEInfusion.

Usage

ModelODEInfusion(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list()
)

Arguments

ModelODEInfusionDoseInEquation

Description

The class ModelODEInfusionDoseInEquation is used to defined a ModelODEInfusionDoseInEquation

Usage

ModelODEInfusionDoseInEquation(
  name = character(0),
  modelParameters = list(),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = function() NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  modelODE = function() NULL,
  wrapperModelInfusion = list(),
  solverInputs = list()
)

Arguments

ModelParameter

Description

The class ModelParameter is used to defined the model parameters.

Usage

ModelParameter(
  name = character(0),
  distribution = Distribution(),
  fixedMu = FALSE,
  fixedOmega = FALSE
)

Arguments

Details

ModelParameter

MultiplicativeAlgorithm

Description

The class MultiplicativeAlgorithm implements the multiplicative algorithm.

Usage

MultiplicativeAlgorithm(
  name = character(0),
  modelEquations = list(),
  modelFromLibrary = list(),
  modelParameters = list(),
  modelError = list(),
  optimizer = character(0),
  optimizerParameters = list(),
  outputs = list(),
  designs = list(),
  fimType = character(0),
  fim = Fim(),
  odeSolverParameters = list(),
  optimisationDesign = list(),
  optimisationAlgorithmOutputs = list(),
  lambda = 0,
  delta = 0,
  numberOfIterations = 0,
  weightThreshold = 0,
  showProcess = FALSE,
  multiplicativeAlgorithmOutputs = list()
)

Arguments

Function MultiplicativeAlgorithm_Rcpp

Description

Run the MultiplicativeAlgorithm_Rcpp in Rcpp.

Usage

MultiplicativeAlgorithm_Rcpp(
  fisherMatrices_input,
  numberOfFisherMatrices_input,
  weights_input,
  numberOfParameters_input,
  dim_input,
  lambda_input,
  delta_input,
  iterationInit_input
)

Arguments

Value

The list output with the outputs of the MultiplicativeAlgorithm_Rcpp.

Normal

Description

The class Normal implements the Normal distribution.

Usage

Normal(name = character(0), mu = 0, omega = 0)

Arguments

Optimization

Description

The class Optimization implements the Optimization.

Usage

Optimization(
  name = character(0),
  modelEquations = list(),
  modelFromLibrary = list(),
  modelParameters = list(),
  modelError = list(),
  optimizer = character(0),
  optimizerParameters = list(),
  outputs = list(),
  designs = list(),
  fimType = character(0),
  fim = Fim(),
  odeSolverParameters = list(),
  optimisationDesign = list(),
  optimisationAlgorithmOutputs = list()
)

Arguments

PFIMProject

Description

The class PFIMProject implements the PFIM project.

Usage

PFIMProject(
  name = character(0),
  modelEquations = list(),
  modelFromLibrary = list(),
  modelParameters = list(),
  modelError = list(),
  optimizer = character(0),
  optimizerParameters = list(),
  outputs = list(),
  designs = list(),
  fimType = character(0),
  fim = Fim(),
  odeSolverParameters = list()
)

Arguments

PGBOAlgorithm

Description

The class PGBOAlgorithm implements the PGBO algorithm.

Usage

PGBOAlgorithm(
  name = character(0),
  modelEquations = list(),
  modelFromLibrary = list(),
  modelParameters = list(),
  modelError = list(),
  optimizer = character(0),
  optimizerParameters = list(),
  outputs = list(),
  designs = list(),
  fimType = character(0),
  fim = Fim(),
  odeSolverParameters = list(),
  optimisationDesign = list(),
  optimisationAlgorithmOutputs = list(),
  N = numeric(0),
  muteEffect = numeric(0),
  maxIteration = numeric(0),
  purgeIteration = numeric(0),
  seed = numeric(0),
  showProcess = FALSE
)

Arguments

PSOAlgorithm

Description

The class PSOAlgorithm implements the PSO algorithm.

Usage

PSOAlgorithm(
  name = character(0),
  modelEquations = list(),
  modelFromLibrary = list(),
  modelParameters = list(),
  modelError = list(),
  optimizer = character(0),
  optimizerParameters = list(),
  outputs = list(),
  designs = list(),
  fimType = character(0),
  fim = Fim(),
  odeSolverParameters = list(),
  optimisationDesign = list(),
  optimisationAlgorithmOutputs = list(),
  maxIteration = numeric(0),
  populationSize = numeric(0),
  seed = numeric(0),
  personalLearningCoefficient = numeric(0),
  globalLearningCoefficient = numeric(0),
  showProcess = FALSE
)

Arguments

PopulationFim

Description

The class PopulationFim represents and stores information for the PopulationFim.

Usage

PopulationFim(
  fisherMatrix = numeric(0),
  fixedEffects = numeric(0),
  varianceEffects = numeric(0),
  SEAndRSE = list(),
  condNumberFixedEffects = 0,
  condNumberVarianceEffects = 0,
  shrinkage = numeric(0)
)

Arguments

Proportional

Description

The class Proportional is used to defined a model error.

Usage

Proportional(
  output = character(0),
  equation = expression(sigmaSlope),
  derivatives = list(),
  sigmaInter = 0,
  sigmaSlope = 0,
  sigmaInterFixed = FALSE,
  sigmaSlopeFixed = FALSE,
  cError = 1
)

Arguments

Generate optimization report

Description

Generate optimization report

Report: generate the report.

Arguments

Value

Generated report.

The html report of the design evaluation or optimization.

SamplingTimeConstraints

Description

The class "SamplingTimeConstraints" implements the constraints for the sampling times.

Usage

SamplingTimeConstraints(
  outcome = character(0),
  initialSamplings = 0,
  fixedTimes = 0,
  numberOfsamplingsOptimisable = 0,
  samplingsWindows = list(),
  numberOfTimesByWindows = 0,
  minSampling = 0
)

Arguments

SamplingTimes

Description

The class SamplingTimes is used to defined SamplingTimes.

Usage

SamplingTimes(outcome = character(0), samplings = numeric(0))

Arguments

SimplexAlgorithm

Description

The class SimplexAlgorithm implements the Simplex algorithm.

Usage

SimplexAlgorithm(
  name = character(0),
  modelEquations = list(),
  modelFromLibrary = list(),
  modelParameters = list(),
  modelError = list(),
  optimizer = character(0),
  optimizerParameters = list(),
  outputs = list(),
  designs = list(),
  fimType = character(0),
  fim = Fim(),
  odeSolverParameters = list(),
  optimisationDesign = list(),
  optimisationAlgorithmOutputs = list(),
  pctInitialSimplexBuilding = numeric(0),
  maxIteration = numeric(0),
  seed = numeric(0),
  tolerance = numeric(0),
  showProcess = FALSE
)

Arguments

adjustGradient: adjust the gradient for the log normal distribution.

Description

adjustGradient: adjust the gradient for the log normal distribution.

Arguments

Value

The adjusted gradient of the model responses.

getArmAdministration: get the administration parameters of an arm.

Description

getArmAdministration: get the administration parameters of an arm.

Arguments

Value

A list giving the administration parameters of an arm.

checkSamplingTimeConstraintsForMetaheuristic

Description

checkSamplingTimeConstraintsForMetaheuristic

Arguments

Value

A boolean TRUE/FALSE, with a message error if FALSE.

checkValiditySamplingConstraint: check if the constraints used for the design optimization are valid.

Description

checkValiditySamplingConstraint: check if the constraints used for the design optimization are valid.

Arguments

Value

A boolean TRUE / FALSE, if FALSE it also gives an error message.

computeVMat

Description

computeVMat

Usage

computeVMat(varParam1, varParam2, invCholV)

Arguments

Value

VMat

constraintsTableForReport: table of the PGBOAlgorithm constraints for the report.

Description

constraintsTableForReport: table of the PGBOAlgorithm constraints for the report.

constraintsTableForReport: table of the PSOAlgorithm constraints for the report.

constraintsTableForReport: table of the SimplexAlgorithm constraints for the report.

constraintsTableForReport

constraintsTableForReport: table of the MultiplicativeAlgorithm constraints for the report.

Arguments

Value

The table for the constraints in the arms.

The table for the constraints in the arms.

The table for the constraints in the arms.

armsConstraintsTable

The table for the constraints in the arms.

convertPKModelAnalyticToPKModelODE: conversion from analytic to ode

Description

convertPKModelAnalyticToPKModelODE: conversion from analytic to ode

convertPKModelAnalyticToPKModelODE: conversion from analytic to ode

convertPKModelAnalyticToPKModelODE: conversion from analytic infusion to ode

Arguments

define the type of Fisher information matrix: population, individual or Bayesian

Description

define the type of Fisher information matrix: population, individual or Bayesian

Arguments

Value

An object Fim.

defineModelAdministration: define the administration

Description

defineModelAdministration: define the administration

defineModelAdministration: define the administration

defineModelAdministration: define the administration

defineModelAdministration: define the administration

defineModelAdministration: define the administration

defineModelAdministration: define the administration

defineModelAdministration: define the administration

Arguments

Value

The model with samplings, solverInputs

The model with samplings, solverInputs

The model with samplings, solverInputs

The model with updated slots.

The model with samplings, solverInputs

The model with samplings, solverInputs

The model with updated slots.

defineModelEquationsFromLibraryOfModel: define the model equations giving the models in the library of models.

Description

defineModelEquationsFromLibraryOfModel: define the model equations giving the models in the library of models.

Arguments

Value

A list giving the model equations.

defineModelType: define the class of the model to be evaluated.

Description

defineModelType: define the class of the model to be evaluated.

Arguments

Value

An object Model giving the model to be evaluated with its modelParameters, odeSolverParameters, modelError, modelEquations.

defineModelWrapper: define the model wrapper for the ode solver

Description

defineModelWrapper: define the model wrapper for the ode solver

defineModelWrapper: define the model wrapper for the ode solver

defineModelWrapper: define the model wrapper for the ode solver

defineModelWrapper: define the model wrapper for the ode solver

defineModelWrapper: define the model wrapper for the ode solver

defineModelWrapper: define the model wrapper for the ode solver

defineModelWrapper: define the model wrapper for the ode solver

defineModelWrapper: define the model wrapper for the ode solver

Arguments

Value

The model with wrapperModelAnalytic, functionArgumentsModelAnalytic, functionArgumentsSymbolModelAnalytic, outputNames, outcomesWithAdministration

The model with wrapperModelAnalyticInfusion, functionArgumentsModelAnalyticInfusion, functionArgumentsSymbolModelAnalyticInfusion, outputNames, outcomesWithAdministration

The model with wrapperModelAnalyticInfusion, functionArgumentsModelAnalyticInfusion, functionArgumentsSymbolModelAnalyticInfusion, outputNames, outcomesWithAdministration

The model with wrapperModelAnalytic, functionArgumentsModelAnalytic, functionArgumentsSymbolModelAnalytic, outputNames, outcomesWithAdministration

The model with updated slots.

The model with the updated slots.

The model with the updated slots.

The model with updated slots.

Define optimization algorithm

Description

Define optimization algorithm

Arguments

Value

An optimization algorithm.

definePKModel: define a PK model from library of model

Description

definePKModel: define a PK model from library of model

definePKModel ModelAnalyticInfusion

definePKModel

definePKModel

definePKModel: define PK model ode bolus

definePKModel: define a PK model from library of model

definePKModel: define a PK model from library of model

definePKModel: define PK model ode bolus

Arguments

definePKPDModel: define a PKPD model from library of model

Description

definePKPDModel: define a PKPD model from library of model

definePKPDModel: define a PKPD model from library of model

definePKPDModel ModelAnalyticInfusion, ModelAnalytic

definePKPDModel ModelAnalyticInfusion, ModelODE

definePKPDModel

definePKPDModel

definePKPDModel

definePKPDModel: define a PKPD model from library of model

Arguments

evaluateArm: evaluation of the model with the arm parameters.

Description

evaluateArm: evaluation of the model with the arm parameters.

Arguments

Value

The object arm with the slots evaluationModel, evaluationGradients, evaluationVariance and evaluationFim.

evaluateDesign: evaluation of a design.

Description

evaluateDesign: evaluation of a design.

Arguments

Value

The object Design with its evaluation results.

evaluateErrorModelDerivatives; evaluate the derivatives of the model error.

Description

evaluateErrorModelDerivatives; evaluate the derivatives of the model error.

Arguments

Value

The matrices sigmaDerivatives and errorVariance.

evaluateFim: evaluation of the Fim

Description

evaluateFim: evaluation of the Fim

evaluateFim: evaluation of the Fim

evaluateFim: evaluation of the Fim

Arguments

Value

The object Fim with the fisherMatrix and the shrinkage.

The object IndividualFim with the fisherMatrix and the shrinkage.

The object IndividualFim with the fisherMatrix and the shrinkage.

evaluateInitialConditions: evaluate the initial conditions.

Description

evaluateInitialConditions: evaluate the initial conditions.

evaluateInitialConditions: evaluate the initial conditions.

evaluateInitialConditions: evaluate the initial conditions.

Arguments

Value

A list giving the evaluated initial conditions.

evaluateModel: evaluate the model

Description

evaluateModel: evaluate the model

evaluateModel: evaluate the ModelAnalyticInfusion

evaluateModel: evaluate the ModelAnalyticInfusion

evaluateModel: evaluate the ModelAnalyticInfusion

evaluateModel

evaluateModel

evaluateModel: evaluate the model

evaluateModel: evaluate the model

evaluateModel

Arguments

Value

A list of dataframes that contains the results for the evaluation of the model.

A list of dataframes that contains the results for the evaluation of the model.

A list of dataframes that contains the results for the evaluation of the model.

A list of dataframes that contains the results for the evaluation of the model.

A list of dataframes that contains the evaluation of the model.

A data frame giving the output of the model evaluation.

A list of dataframes that contains the results for the evaluation of the model.

A list of dataframes that contains the results for the evaluation of the model.

A data frame giving the output of the model evaluation.

evaluateModelGradient: evaluate the gradient of the model

Description

evaluateModelGradient: evaluate the gradient of the model

Arguments

Value

A data frame that contains the gradient of the model.

evaluateModelVariance: evaluate the variance of the model

Description

evaluateModelVariance: evaluate the variance of the model

Arguments

Value

A list giving errorVariance and sigmaDerivatives.

evaluateVarianceFIM: evaluate the variance

Description

evaluateVarianceFIM: evaluate the variance

evaluateVarianceFIM: evaluate the variance

evaluateVarianceFIM: evaluate the variance

Arguments

Value

The matrices MFbeta and V.

The matrices MFbeta and V.

The matrices MFVar and V.

finiteDifferenceHessian: compute the Hessian

Description

finiteDifferenceHessian: compute the Hessian

Arguments

Value

The model with the slots parametersForComputingGradient with XcolsInv, shifted, frac.

Compute the fisher.simplex

Description

Compute the fisher.simplex

Arguments

Value

A list giving the results of the optimization.

Compute the fun.amoeba

Description

Compute the fun.amoeba

Usage

fun.amoeba(p, y, ftol, itmax, funk, outcomes, data, showProcess)

Arguments

Value

fun.amoeba

generateDosesCombination: generate the combination for the doses.

Description

generateDosesCombination: generate the combination for the doses.

Arguments

Value

dosesForFIMs, numberOfDoses used in the design optimization.

Generate FIMs from constraints

Description

Generate FIMs from constraints

Arguments

Value

A list containing FIMs from constraints.

generateReportEvaluation: generate the report for the model evaluation.

Description

generateReportEvaluation: generate the report for the model evaluation.

generateReportEvaluation: generate the report for the model evaluation.

generateReportEvaluation: generate the report for the model evaluation.

Arguments

Value

The html report for the design evaluation.

The html report for the model evaluation.

The html report for the model evaluation.

generateReportOptimization: generate the report for the design optimization.

Description

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

generateReportOptimization: generate the report for the design optimization.

Arguments

Value

The html report for the design optimization.

The html report for the design optimization.

The html report.

The html report.

The html report.

The html report.

The html report.

The html report.

The html report.

The html report.

The html report.

The html report.

generateSamplingTimesCombination: generate the combination for the samplings.

Description

generateSamplingTimesCombination: generate the combination for the samplings.

Arguments

Value

samplingTimesCombinations used in the design optimization.

generateSamplingsFromSamplingConstraints

Description

generateSamplingsFromSamplingConstraints

Arguments

Value

A list intervalsConstraints.

getArmConstraints: get the administration and sampling time constraints for the MultiplicativeAlgorithm.

Description

getArmConstraints: get the administration and sampling time constraints for the MultiplicativeAlgorithm.

getArmConstraints: get the administration and sampling time constraints for the FedorovWynnAlgorithm.

getArmConstraints: get the administration and sampling time constraints for the SimplexAlgorithm.

getArmConstraints: get the administration and sampling time constraints for the PSOAlgorithm.

getArmConstraints: get the administration and sampling time constraints for the PGBOAlgorithm.

Arguments

Value

A list giving the administration and sampling time constraints for the MultiplicativeAlgorithm.

A list giving the administration and sampling time constraints for the FedorovWynnAlgorithm.

A list giving the administration and sampling time constraints for the SimplexAlgorithm.

A list giving the administration and sampling time constraints for the PSOAlgorithm.

A list giving the administration and sampling time constraints for the PGBOAlgorithm.

getArmData: extract arm data for The Report

Description

getArmData: extract arm data for The Report

Arguments

Value

A list giving the name, Number of subjects, Outcome, Dose and Sampling times of the arm.

getCorrelationMatrix : get the correlation matrix

Description

getCorrelationMatrix : get the correlation matrix

getCorrelationMatrix : get the correlation matrix

Arguments

Value

The correlation matrix

The Dcriterion

getDcriterion : get the Dcriterion

Description

getDcriterion : get the Dcriterion

getDcriterion : get the Dcriterion

Arguments

Value

The Dcriterion of the FIM.

The Dcriterion

getDeterminant: get the determinant

Description

getDeterminant: get the determinant

getDeterminant: get the determinant

Arguments

Value

The determinant of the FIM.

The determinant

getFim: get the Fisher matrix.

Description

getFim: get the Fisher matrix.

Arguments

Value

The matrices fisherMatrix, fixedEffects, varianceEffects.

getFisherMatrix: display the Fisher matrix components

Description

getFisherMatrix: display the Fisher matrix components

getFisherMatrix: display the Fisher matrix components

Arguments

Value

The matrices fisherMatrix, fixedEffects, varianceEffects.

The matrices fisherMatrix, fixedEffects, varianceEffects.

getListLastName: routine to get the names of last element of a nested list.

Description

getListLastName: routine to get the names of last element of a nested list.

Usage

getListLastName(list)

Arguments

Value

The names of last element.

getModelErrorData: get the parameters sigma slope and sigma inter (used for the report).

Description

getModelErrorData: get the parameters sigma slope and sigma inter (used for the report).

Arguments

Value

A list of dataframe with outcome, type of model error and sigma slope and inter.

getModelParametersData: get model parameters data for report.

Description

getModelParametersData: get model parameters data for report.

Arguments

Value

A data frame with the data of all the parameters.

getRSE: get the RSE

Description

getRSE: get the RSE

getRSE: get the RSE

Arguments

Value

The RSE of the parameters.

The RSE

getSE: get the SE

Description

getSE: get the SE

getSE: get the SE

Arguments

Value

The SE of the parameters.

The SE.

getSamplingData: extract sampling times and max sampling time used for plot.

Description

getSamplingData: extract sampling times and max sampling time used for plot.

Arguments

Value

A list giving the samplingTimes object, the vector samplings and the double samplingMax.

getShrinkage: get the shrinkage

Description

getShrinkage: get the shrinkage

getShrinkage: get the shrinkage

Arguments

Value

The shrinkage of the FIM.

The shrinkage

Optimization PGBOAlgorithm

Description

Optimization PGBOAlgorithm

Optimization PSOAlgorithm

Optimization SimplexAlgorithm

Optimization FedorovWynnAlgorithm

Optimization MultiplicativeAlgorithm

Arguments

Value

The object optimizationObject with the slots updated.

The object optimizationObject with the slots updated.

The object optimizationObject with the slots updated.

The object optimizationObject with the slots updated.

The object optimizationObject with the slots updated.

plotEvaluation: plots for the evaluation of the model responses.

Description

plotEvaluation: plots for the evaluation of the model responses.

Arguments

Value

All the plots for the evaluation of the model responses.

plotEvaluationResults: process for the evaluation of the responses.

Description

plotEvaluationResults: process for the evaluation of the responses.

Arguments

Value

A list giving the plot of the evaluation of the model responses.

plotEvaluationSI: process for the evaluation of the gradient of the responses.

Description

plotEvaluationSI: process for the evaluation of the gradient of the responses.

Arguments

Value

A list giving the plot of the evaluation of gradient of the model responses.

Plot frequencies for the FedorovWynn algorithm

Description

Plot frequencies for the FedorovWynn algorithm

Arguments

Value

Graph of the optimal frequencies.

plotFrequenciesFedorovWynnAlgorithm

Description

plotFrequenciesFedorovWynnAlgorithm

Arguments

Value

plotFrequenciesFedorovWynnAlgorithm

Plot relative standard errors

Description

Plot relative standard errors

plotRSE: bar plot of the RSE.

Arguments

Value

Graph of relative standard errors

The bar plot of the RSE.

plotRSEFIM: barplot for the RSE

Description

plotRSEFIM: barplot for the RSE

plotRSEFIM: barplot for the RSE

plotRSEFIM: barplot for the RSE

Arguments

Value

The bar plot of the RSE.

The bar plot of the RSE.

The bar plot of the RSE.

Plot standard errors

Description

Plot standard errors

plotSE: bar plot of the SE.

Arguments

Value

Graph of standard errors

The bar plot of the SE.

plotSEFIM: barplot for the SE

Description

plotSEFIM: barplot for the SE

plotSEFIM: barplot for the SE

plotSEFIM: barplot for the SE

Arguments

Value

The bar plot of the SE.

The bar plot of the SE.

The bar plot of the SE.

Plot sensitivity indices.

Description

Plot sensitivity indices.

plotSensitivityIndices: plots for the evaluation of the gradient of the model responses.

Arguments

Value

Graph of sensitivity indices.

All the plots for the evaluation of the gradient of the model responses.

plotShrinkage: plot the shrinkage values.

Description

plotShrinkage: plot the shrinkage values.

Arguments

Value

The bar plot of the shrinkage.

Plot weights for the multiplicative algorithm

Description

Plot weights for the multiplicative algorithm

Arguments

Value

Plot of weights

plotWeightsMultiplicativeAlgorithm: plot the optimal weight.

Description

plotWeightsMultiplicativeAlgorithm: plot the optimal weight.

Arguments

Value

The graph plotWeight.

processArmEvaluationResults: process for the evaluation of an arm.

Description

processArmEvaluationResults: process for the evaluation of an arm.

Arguments

Value

A list of ggplot object giving the plot of the responses ans the gradient responses of the the model.

processArmEvaluationSI: process for the evaluation of the gradient of the responses.

Description

processArmEvaluationSI: process for the evaluation of the gradient of the responses.

Arguments

Value

A list giving the ggplot object of the plots of the gradient.

replaceVariablesLibraryOfModels: replace variable in the LibraryOfModels

Description

replaceVariablesLibraryOfModels: replace variable in the LibraryOfModels

Usage

replaceVariablesLibraryOfModels(text, old, new)

Arguments

Value

text with new string

Run optimization

Description

Run optimization

run: run the evaluation of a design.

Arguments

Value

The optimization design results.

The object Evaluation giving the design evaluation.

setEvaluationFim: set the Fim results.

Description

setEvaluationFim: set the Fim results.

setEvaluationFim: set the Fim results.

setEvaluationFim: set the Fim results.

Arguments

Value

The object Fim with its fisherMatrix, fixedEffects, shrinkage, condNumberFixedEffects, SEAndRSE.

The object IndividualFim with its fisherMatrix, fixedEffects, shrinkage, condNumberFixedEffects, SEAndRSE.

The object PopulationFim with its fisherMatrix, fixedEffects, shrinkage, condNumberFixedEffects, SEAndRSE.

setOptimalArms: set the optimal arms of an optimization algorithm.

Description

setOptimalArms: set the optimal arms of an optimization algorithm.

setOptimalArms: set the optimal arms of an optimization algorithm.

setOptimalArms: set the optimal arms of an optimization algorithm.

setOptimalArms: set the optimal arms of an optimization algorithm.

setOptimalArms: set the optimal arms of an optimization algorithm.

setOptimalArms: set the optimal arms of an optimization algorithm.

Arguments

Value

The optimal arms.

The optimal arms.

The list optimalArms.

The list optimalArms.

The list optimalArms.

The list optimalArms.

setSamplingConstraintForOptimization: set the sampling time constraints for an arm for the design optimization.

Description

setSamplingConstraintForOptimization: set the sampling time constraints for an arm for the design optimization.

Arguments

Value

The arm with the sampling time constraint for the design optimization.

Show optimization results

Description

Show optimization results

show: show the evaluation in the R console.

Arguments

Value

Prints results to console.

The show of the evaluation of the design.

showFIM: show the Fim in the R console.

Description

showFIM: show the Fim in the R console.

showFIM: show the Fim in the R console.

showFIM: show the Fim in the R console.

Arguments

Value

The fisherMatrix, fixedEffects, Determinant, condition numbers and D-criterion, Shrinkage and Parameters estimation

The fisherMatrix, fixedEffects, Determinant, condition numbers and D-criterion, Shrinkage and Parameters estimation

The fisherMatrix, fixedEffects, Determinant, condition numbers and D-criterion, Shrinkage and Parameters estimation

tablesForReport: generate the table for the report.

Description

tablesForReport: generate the table for the report.

tablesForReport: generate the table for the report.

tablesForReport: generate the table for the report.

Arguments

Value

fixedEffectsTable, FIMCriteriaTable, SEAndRSETable.

fixedEffectsTable, FIMCriteriaTable, SEAndRSETable.

fixedEffectsTable, FIMCriteriaTable, SEAndRSETable.

updateSamplingTimes: update sampling times for plotting used for plot

Description

updateSamplingTimes: update sampling times for plotting used for plot

Arguments

Value

The updated sampling times.