| Object-oriented implementation of CRM designs | crmPack-package crmPack |
| The method combining two atomic stopping rules | &,Stopping,Stopping-method |
| The method combining an atomic and a stopping list | &,Stopping,StoppingAll-method |
| The method combining a stopping list and an atomic | &,StoppingAll,Stopping-method |
| Class for All models This is a class where all models inherit. | .AllModels AllModels-class |
| Approximate posterior with (log) normal distribution | approximate approximate,Samples-method |
| as.list method for the "GeneralData" class | as.list,GeneralData-method |
| Compute the biomarker level for a given dose, given model and samples | biomLevel biomLevel,numeric,DualEndpoint,Samples-method |
| The virtual class for cohort sizes | CohortSize-class |
| Initialization function for "CohortSizeConst" | CohortSizeConst |
| Constant cohort size | .CohortSizeConst CohortSizeConst-class |
| Initialization function for "CohortSizeDLT" | CohortSizeDLT |
| Cohort size based on number of DLTs | .CohortSizeDLT CohortSizeDLT-class |
| Initialization function for "CohortSizeMax" | CohortSizeMax |
| Size based on maximum of multiple cohort size rules | .CohortSizeMax CohortSizeMax-class |
| Initialization function for "CohortSizeMin" | CohortSizeMin |
| Size based on minimum of multiple cohort size rules | .CohortSizeMin CohortSizeMin-class |
| Initialization function for "CohortSizeParts" | CohortSizeParts |
| Cohort size based on the parts | .CohortSizeParts CohortSizeParts-class |
| Initialization function for "CohortSizeRange" | CohortSizeRange |
| Cohort size based on dose range | .CohortSizeRange CohortSizeRange-class |
| Open the example pdf for crmPack | crmPackExample |
| Open the browser with help pages for crmPack | crmPackHelp |
| Initialization function for the "Data" class | Data |
| Class for the data input | .Data Data-class |
| Initialization function for the "DataDual" class | DataDual |
| Class for the dual endpoint data input | .DataDual DataDual-class |
| Initialization function for the "DataMixture" class | DataMixture |
| Class for the data with mixture sharing | .DataMixture DataMixture-class |
| Initialization function for the "DataParts" class | DataParts |
| Class for the data with two study parts | .DataParts DataParts-class |
| Initialization function for "Design" | Design |
| Class for the CRM design | .Design Design-class |
| Compute the doses for a given probability, given model and samples | dose dose,numeric,Model,Samples-method dose,numeric,ModelTox,missing-method dose,numeric,ModelTox,Samples-method |
| Initialization function for "DualDesign" | DualDesign |
| Class for the dual-endpoint CRM design | .DualDesign DualDesign-class |
| Initialization function for the "DualEndpoint" class | DualEndpoint |
| General class for the dual endpoint model | .DualEndpoint DualEndpoint-class |
| Initialization function for the "DualEndpointBeta" class | DualEndpointBeta |
| Dual endpoint model with beta function for dose-biomarker relationship | .DualEndpointBeta DualEndpointBeta-class |
| Initialization function for the "DualEndpointEmax" class | DualEndpointEmax |
| Dual endpoint model with emax function for dose-biomarker relationship | .DualEndpointEmax DualEndpointEmax-class |
| Initialization function for the "DualEndpointRW" class | DualEndpointRW |
| Dual endpoint model with RW prior for biomarker | .DualEndpointRW DualEndpointRW-class |
| Initialization function for 'DualResponsesDesign" | DualResponsesDesign |
| This is a class of design based on DLE responses using the 'LogisticIndepBeta' model model and efficacy responses using 'ModelEff' model class without DLE and efficacy samples. It contain all slots in 'RuleDesign' and 'TDDesign' class object | .DualResponsesDesign DualResponsesDesign-class |
| Initialization function for 'DualResponsesSamplesDesign" | DualResponsesSamplesDesign |
| This is a class of design based on DLE responses using the 'LogisticIndepBeta' model model and efficacy responses using 'ModelEff' model class with DLE and efficacy samples.It contain all slots in 'RuleDesign' and 'TDsamplesDesign' class object | .DualResponsesSamplesDesign DualResponsesSamplesDesign-class |
| Initialization function for "DualSimulations" | DualSimulations |
| Class for the simulations output from dual-endpoint model based designs | .DualSimulations DualSimulations-class |
| Class for the summary of dual-endpoint simulations output | .DualSimulationsSummary DualSimulationsSummary-class |
| Initialization function for the "EffFlexi" class | EffFlexi |
| Class for the efficacy model in flexible form for prior expressed in form of pseudo data | .EffFlexi EffFlexi-class |
| Initialization function for the "Effloglog" class | Effloglog |
| Class for the linear log-log efficacy model using pseudo data prior | .Effloglog Effloglog-class |
| Obtain hypothetical trial course table for a design | examine examine,Design-method examine,RuleDesign-method |
| Compute the expected efficacy based on a given dose, a given pseudo Efficacy log-log model and a given efficacy sample | ExpEff ExpEff,numeric,EffFlexi,Samples-method ExpEff,numeric,Effloglog,missing-method ExpEff,numeric,Effloglog,Samples-method |
| Fit method for the Samples class | fit fit,Samples,DualEndpoint,DataDual-method fit,Samples,EffFlexi,DataDual-method fit,Samples,Effloglog,DataDual-method fit,Samples,LogisticIndepBeta,Data-method fit,Samples,Model,Data-method |
| Get the fiited values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples | fitGain fitGain,ModelTox,Samples,ModelEff,Samples,DataDual-method |
| Compute the gain value with a given dose level, given a pseudo DLE model, a DLE sample, a pseudo Efficacy log-log model and a Efficacy sample | gain gain,numeric,ModelTox,missing,Effloglog,missing-method gain,numeric,ModelTox,Samples,EffFlexi,Samples-method gain,numeric,ModelTox,Samples,Effloglog,Samples-method |
| Class for general data input | .GeneralData GeneralData-class |
| No Intitialization function for this General class for model input | .GeneralModel GeneralModel-class |
| Initialization function for "GeneralSimulations" | GeneralSimulations |
| General class for the simulations output | .GeneralSimulations GeneralSimulations-class |
| Class for the summary of general simulations output | .GeneralSimulationsSummary GeneralSimulationsSummary-class |
| Get specific parameter samples and produce a data.frame | get,Samples,character-method |
| Extracting efficacy responses for subjects without or with a DLE. This is a class where we separate efficacy responses with or without a DLE. It outputs the efficacy responses and their corresponding dose levels treated at in two categories (with or without DLE) | getEff getEff,DataDual-method |
| Get the minimal informative unimodal beta distribution | getMinInfBeta |
| Initialization function for "IncrementMin" | IncrementMin |
| Max increment based on minimum of multiple increment rules | .IncrementMin IncrementMin-class |
| The virtual class for controlling increments | Increments-class |
| Initialization function for "IncrementsNumDoseLevels" | IncrementsNumDoseLevels |
| Increments control based on number of dose levels | .IncrementsNumDoseLevels IncrementsNumDoseLevels-class |
| Initialization function for "IncrementsRelative" | IncrementsRelative |
| Increments control based on relative differences in intervals | .IncrementsRelative IncrementsRelative-class |
| Initialization function for "IncrementsRelativeDLT" | IncrementsRelativeDLT |
| Increments control based on relative differences in terms of DLTs | .IncrementsRelativeDLT IncrementsRelativeDLT-class |
| Initialization function for "IncrementsRelativeParts" | IncrementsRelativeParts |
| Increments control based on relative differences in intervals, with special rules for part 1 and beginning of part 2 | .IncrementsRelativeParts IncrementsRelativeParts-class |
| Initialization method for the "DualEndpointOld" class | initialize,DualEndpointOld-method |
| Intialization function for "LogisticIndepBeta" class | LogisticIndepBeta |
| No initialization function Standard logistic model with prior in form of pseudo data | .LogisticIndepBeta LogisticIndepBeta-class |
| Initialization function for the "LogisticKadane" class | LogisticKadane |
| Reparametrized logistic model | .LogisticKadane LogisticKadane-class |
| Initialization function for the "LogisticLogNormal" class | LogisticLogNormal |
| Standard logistic model with bivariate (log) normal prior | .LogisticLogNormal LogisticLogNormal-class |
| Initialization function for the "LogisticLogNormalMixture" class | LogisticLogNormalMixture |
| Standard logistic model with online mixture of two bivariate log normal priors | .LogisticLogNormalMixture LogisticLogNormalMixture-class |
| Initialization function for the "LogisticLogNormalSub" class | LogisticLogNormalSub |
| Standard logistic model with bivariate (log) normal prior with substractive dose standardization | .LogisticLogNormalSub LogisticLogNormalSub-class |
| Initialization function for the "LogisticNormal" class | LogisticNormal |
| Standard logistic model with bivariate normal prior | .LogisticNormal LogisticNormal-class |
| Initialization function for the "LogisticNormalFixedMixture" class | LogisticNormalFixedMixture |
| Standard logistic model with fixed mixture of multiple bivariate (log) normal priors | .LogisticNormalFixedMixture LogisticNormalFixedMixture-class |
| Initialization function for the "LogisticNormalMixture" class | LogisticNormalMixture |
| Standard logistic model with flexible mixture of two bivariate normal priors | .LogisticNormalMixture LogisticNormalMixture-class |
| Shorthand for logit function | logit |
| Helper function for value matching with tolerance | %~% matchTolerance |
| Determine the maximum possible next dose | maxDose maxDose,IncrementMin,Data-method maxDose,IncrementsNumDoseLevels,Data-method maxDose,IncrementsRelative,Data-method maxDose,IncrementsRelativeDLT,Data-method maxDose,IncrementsRelativeParts,DataParts-method |
| "MAX" combination of cohort size rules | maxSize maxSize,CohortSize-method |
| Obtain posterior samples for all model parameters | mcmc mcmc,Data,LogisticIndepBeta,McmcOptions-method mcmc,DataDual,EffFlexi,McmcOptions-method mcmc,DataDual,Effloglog,McmcOptions-method mcmc,DataMixture,GeneralModel,McmcOptions-method mcmc,GeneralData,GeneralModel,McmcOptions-method |
| Initialization function for the "McmcOptions" class | McmcOptions |
| Class for the three canonical MCMC options | .McmcOptions McmcOptions-class |
| Construct a minimally informative prior | MinimalInformative |
| "MIN" combination of cohort size rules | minSize minSize,CohortSize-method |
| Class for the model input | .Model Model-class |
| No Initialization function class for Efficacy models using pseudo data prior | .ModelEff ModelEff-class |
| Class of models using expressing their prior in form of Pseudo data | .ModelPseudo ModelPseudo-class |
| No intialization function Class for DLE models using pseudo data prior. This is a class of DLE (dose-limiting events) models/ toxicity model which contains all DLE models for which their prior are specified in form of pseudo data (as if there is some data before the trial starts). It inherits all slots from 'ModelPseudo' | .ModelTox ModelTox-class |
| Multiple plot function | multiplot |
| Find the next best dose | nextBest nextBest,NextBestDualEndpoint,numeric,Samples,DualEndpoint,Data-method nextBest,NextBestMaxGain,numeric,missing,ModelTox,DataDual-method nextBest,NextBestMaxGainSamples,numeric,Samples,ModelTox,DataDual-method nextBest,NextBestMTD,numeric,Samples,Model,Data-method nextBest,NextBestNCRM,numeric,Samples,Model,Data-method nextBest,NextBestNCRM,numeric,Samples,Model,DataParts-method nextBest,NextBestTD,numeric,missing,LogisticIndepBeta,Data-method nextBest,NextBestTDsamples,numeric,Samples,LogisticIndepBeta,Data-method nextBest,NextBestThreePlusThree,missing,missing,missing,Data-method |
| The virtual class for finding next best dose | NextBest-class |
| Initialization function for "NextBestDualEndpoint" | NextBestDualEndpoint |
| The class with the input for finding the next dose based on the dual endpoint model | .NextBestDualEndpoint NextBestDualEndpoint-class |
| Initialization function for the class 'NextBestMaxGain' | NextBestMaxGain |
| Next best dose with maximum gain value based on a pseudo DLE and efficacy model without samples | .NextBestMaxGain NextBestMaxGain-class |
| Initialization function for class "NextBestMaxGainSamples" | NextBestMaxGainSamples |
| Next best dose with maximum gain value based on a pseudo DLE and efficacy model with samples | .NextBestMaxGainSamples NextBestMaxGainSamples-class |
| Initialization function for class "NextBestMTD" | NextBestMTD |
| The class with the input for finding the next best MTD estimate | .NextBestMTD NextBestMTD-class |
| Initialization function for "NextBestNCRM" | NextBestNCRM |
| The class with the input for finding the next dose in target interval | .NextBestNCRM NextBestNCRM-class |
| Initialization function for the class "NextBestTD" | NextBestTD |
| Next best dose based on Pseudo DLE model without sample | .NextBestTD NextBestTD-class |
| Initialization function for class "NextBestTDsamples" | NextBestTDsamples |
| Next best dose based on Pseudo DLE Model with samples | .NextBestTDsamples NextBestTDsamples-class |
| Initialization function for "NextBestThreePlusThree" | NextBestThreePlusThree |
| The class with the input for finding the next dose in target interval | .NextBestThreePlusThree NextBestThreePlusThree-class |
| The method combining two atomic stopping rules | or-Stopping-Stopping |,Stopping,Stopping-method |
| The method combining a stopping list and an atomic | or-Stopping-StoppingAny |,StoppingAny,Stopping-method |
| The method combining an atomic and a stopping list | or-StoppingAny-Stopping |,Stopping,StoppingAny-method |
| Plot method for the "Data" class | plot,Data,missing-method |
| Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples | plot,Data,ModelTox-method |
| Plot method for the "DataDual" class | plot,DataDual,missing-method |
| Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples | plot,DataDual,ModelEff-method |
| Plot dual-endpoint simulations | plot,DualSimulations,missing-method |
| Plot summaries of the dual-endpoint design simulations | plot,DualSimulationsSummary,missing-method |
| Plot simulations | plot,GeneralSimulations,missing-method |
| Graphical display of the general simulation summary | plot,GeneralSimulationsSummary,missing-method |
| Plot for PseudoDualFlexiSimulations | plot,PseudoDualFlexiSimulations,missing-method |
| Plot simulations | plot,PseudoDualSimulations,missing-method |
| Plot the summary of Pseudo Dual Simulations summary | plot,PseudoDualSimulationsSummary,missing-method |
| Plot summaries of the pseudo simulations | plot,PseudoSimulationsSummary,missing-method |
| Plotting dose-toxicity and dose-biomarker model fits | plot,Samples,DualEndpoint-method |
| Plotting dose-toxicity model fits | plot,Samples,Model-method |
| Plot the fitted dose-effcacy curve using a model from 'ModelEff' class with samples | plot,Samples,ModelEff-method |
| Plot the fitted dose-DLE curve using a 'ModelTox' class model with samples | plot,Samples,ModelTox-method |
| Plot summaries of the model-based design simulations | plot,SimulationsSummary,missing-method |
| Plots gtable objects | plot.gtable |
| Plot of the DLE and efficacy curve side by side given a DLE pseudo model, a DLE sample, an efficacy pseudo model and a given efficacy sample | plotDualResponses plotDualResponses,ModelTox,missing,ModelEff,missing-method plotDualResponses,ModelTox,Samples,ModelEff,Samples-method |
| Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, a DLE sample, a given efficacy pseudo model and an efficacy sample | plotGain plotGain,ModelTox,missing,ModelEff,missing-method plotGain,ModelTox,Samples,ModelEff,Samples-method |
| Compute the probability for a given dose, given model and samples | prob prob,numeric,Model,Samples-method prob,numeric,ModelTox,missing-method prob,numeric,ModelTox,Samples-method |
| Shorthand for probit function | probit |
| Initialization function for the "ProbitLogNormal" class | ProbitLogNormal |
| Probit model with bivariate log normal prior | .ProbitLogNormal ProbitLogNormal-class |
| Initialization function for 'PseudoDualFlexiSimulations' class | PseudoDualFlexiSimulations |
| This is a class which captures the trial simulations design using both the DLE and efficacy responses. The design of model from 'ModelTox' class and the efficacy model from 'EffFlexi' class It contains all slots from 'GeneralSimulations', 'PseudoSimulations' and 'PseudoDualSimulations' object. In comparison to the parent class 'PseudoDualSimulations', it contains additional slots to capture the sigma2betaW estimates. | .PseudoDualFlexiSimulations PseudoDualFlexiSimulations-class |
| Initialization function for 'DualPseudoSimulations' class | PseudoDualSimulations |
| This is a class which captures the trial simulations design using both the DLE and efficacy responses. The design of model from 'ModelTox' class and the efficacy model from 'ModelEff' class (except 'EffFlexi' class). It contains all slots from 'GeneralSimulations' and 'PseudoSimulations' object. In comparison to the parent class 'PseudoSimulations', it contains additional slots to capture the dose-efficacy curve and the sigma2 estimates. | .PseudoDualSimulations PseudoDualSimulations-class |
| Class for the summary of the dual responses simulations using pseudo models | .PseudoDualSimulationsSummary PseudoDualSimulationsSummary-class |
| Initialization function of the 'PseudoSimulations' class | PseudoSimulations |
| This is a class which captures the trial simulations from designs using pseudo model. The design for DLE only responses and model from 'ModelTox' class object. It contains all slots from 'GeneralSimulations' object. Additional slots fit and stopReasons compared to the general class 'GeneralSimulations'. | .PseudoSimulations PseudoSimulations-class |
| Class for the summary of pseudo-models simulations output | .PseudoSimulationsSummary PseudoSimulationsSummary-class |
| Convert prior quantiles (lower, median, upper) to logistic (log) normal model | Quantiles2LogisticNormal |
| A Reference Class to represent sequentially updated reporting objects. | Report |
| Initialization function for "RuleDesign" | RuleDesign |
| Class for rule-based designs | .RuleDesign RuleDesign-class |
| Initialization function for "Samples" | Samples |
| Class for the MCMC output | .Samples Samples-class |
| Compute the number of samples for a given MCMC options triple | sampleSize |
| Helper function to set and save the RNG seed | setSeed |
| Show the summary of the dual-endpoint simulations | show,DualSimulationsSummary-method |
| Show the summary of the simulations | show,GeneralSimulationsSummary-method |
| Show the summary of Pseudo Dual simulations summary | show,PseudoDualSimulationsSummary-method |
| Show the summary of the simulations | show,PseudoSimulationsSummary-method |
| Show the summary of the simulations | show,SimulationsSummary-method |
| Simulate outcomes from a CRM design | simulate,Design-method |
| Simulate outcomes from a dual-endpoint design | simulate,DualDesign-method |
| This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the 'DualResponsesDesign' where DLEmodel used are of 'ModelTox' class object and efficacy model used are of 'ModelEff' class object. In addition, no DLE and efficacy samples are involved or generated in the simulation process | simulate,DualResponsesDesign-method |
| This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the 'DualResponsesSamplesDesign' where DLEmodel used are of 'ModelTox' class object and efficacy model used are of 'ModelEff' class object (special case is 'EffFlexi' class model object). In addition, DLE and efficacy samples are involved or generated in the simulation process | simulate,DualResponsesSamplesDesign-method |
| Simulate outcomes from a rule-based design | simulate,RuleDesign-method |
| This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the 'TDDesign' where model used are of 'ModelTox' class object and no samples are involved. | simulate,TDDesign-method |
| This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the 'TDsamplesDesign' where model used are of 'ModelTox' class object DLE samples are also used | simulate,TDsamplesDesign-method |
| Initialization function for the "Simulations" class | Simulations |
| Class for the simulations output from model based designs | .Simulations Simulations-class |
| Class for the summary of model-based simulations output | .SimulationsSummary SimulationsSummary-class |
| Determine the size of the next cohort | size size,CohortSizeConst,ANY,Data-method size,CohortSizeDLT,ANY,Data-method size,CohortSizeMax,ANY,Data-method size,CohortSizeMin,ANY,Data-method size,CohortSizeParts,ANY,DataParts-method size,CohortSizeRange,ANY,Data-method |
| The virtual class for stopping rules | Stopping-class |
| Initialization function for "StoppingAll" | StoppingAll |
| Stop based on fullfillment of all multiple stopping rules | .StoppingAll StoppingAll-class |
| Initialization function for "StoppingAny" | StoppingAny |
| Stop based on fullfillment of any stopping rule | .StoppingAny StoppingAny-class |
| Initialization function for "StoppingCohortsNearDose" | StoppingCohortsNearDose |
| Stop based on number of cohorts near to next best dose | .StoppingCohortsNearDose StoppingCohortsNearDose-class |
| Initialization function for "StoppingGstarCIRatio" | StoppingGstarCIRatio |
| Stop based on a target ratio, the ratio of the upper to the lower 95% credibility interval of the estimate of the minimum of the dose which gives the maximum gain (Gstar) and the TD end of trial, the dose with probability of DLE equals to the target probability of DLE used at the end of a trial. | .StoppingGstarCIRatio StoppingGstarCIRatio-class |
| Initialization function for "StoppingHighestDose" | StoppingHighestDose |
| Stop when the highest dose is reached | .StoppingHighestDose StoppingHighestDose-class |
| Initialization function for "StoppingList" | StoppingList |
| Stop based on multiple stopping rules | .StoppingList StoppingList-class |
| Initialization function for "StoppingMinCohorts" | StoppingMinCohorts |
| Stop based on minimum number of cohorts | .StoppingMinCohorts StoppingMinCohorts-class |
| Initialization function for "StoppingMinPatients" | StoppingMinPatients |
| Stop based on minimum number of patients | .StoppingMinPatients StoppingMinPatients-class |
| Initialization function for "StoppingMTDdistribution" | StoppingMTDdistribution |
| Stop based on MTD distribution | .StoppingMTDdistribution StoppingMTDdistribution-class |
| Initialization function for "StoppingPatientsNearDose" | StoppingPatientsNearDose |
| Stop based on number of patients near to next best dose | .StoppingPatientsNearDose StoppingPatientsNearDose-class |
| Initialization function for "StoppingTargetBiomarker" | StoppingTargetBiomarker |
| Stop based on probability of target biomarker | .StoppingTargetBiomarker StoppingTargetBiomarker-class |
| Initialization function for "StoppingTargetProb" | StoppingTargetProb |
| Stop based on probability of target tox interval | .StoppingTargetProb StoppingTargetProb-class |
| Initialization function for "StoppingTDCIRatio" | StoppingTDCIRatio |
| Stop based on a target ratio, the ratio of the upper to the lower 95% credibility interval of the estimate of TD end of trial, the dose with probability of DLE equals to the target probability of DLE used at the end of a trial | .StoppingTDCIRatio StoppingTDCIRatio-class |
| Stop the trial? | stopTrial stopTrial,StoppingAll,ANY,ANY,ANY,ANY-method stopTrial,StoppingAny,ANY,ANY,ANY,ANY-method stopTrial,StoppingCohortsNearDose,numeric,ANY,ANY,Data-method stopTrial,StoppingGstarCIRatio,ANY,missing,ModelTox,DataDual-method stopTrial,StoppingGstarCIRatio,ANY,Samples,ModelTox,DataDual-method stopTrial,StoppingHighestDose,numeric,ANY,ANY,Data-method stopTrial,StoppingList,ANY,ANY,ANY,ANY-method stopTrial,StoppingMinCohorts,ANY,ANY,ANY,Data-method stopTrial,StoppingMinPatients,ANY,ANY,ANY,Data-method stopTrial,StoppingMTDdistribution,numeric,Samples,Model,ANY-method stopTrial,StoppingPatientsNearDose,numeric,ANY,ANY,Data-method stopTrial,StoppingTargetBiomarker,numeric,Samples,DualEndpoint,ANY-method stopTrial,StoppingTargetProb,numeric,Samples,Model,ANY-method stopTrial,StoppingTDCIRatio,ANY,missing,ModelTox,ANY-method stopTrial,StoppingTDCIRatio,ANY,Samples,ModelTox,ANY-method |
| Summarize the dual-endpoint design simulations, relative to given true dose-toxicity and dose-biomarker curves | summary,DualSimulations-method |
| Summarize the simulations, relative to a given truth | summary,GeneralSimulations-method |
| Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model. | summary,PseudoDualFlexiSimulations-method |
| Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model) | summary,PseudoDualSimulations-method |
| Summarize the simulations, relative to a given truth | summary,PseudoSimulations-method |
| Summarize the model-based design simulations, relative to a given truth | summary,Simulations-method |
| Initialization function for 'TDDesign' class | TDDesign |
| Design class using DLE responses only based on the pseudo DLE model without sample | .TDDesign TDDesign-class |
| Initialization function for 'TDsamplesDesign' class | TDsamplesDesign |
| This is a class of design based only on DLE responses using the 'LogisticIndepBeta' class model and DLE samples are also used. In addition to the slots in the more simple 'RuleDesign', objects of this class contain: | .TDsamplesDesign TDsamplesDesign-class |
| Creates a new 3+3 design object from a dose grid | ThreePlusThreeDesign |
| Update method for the "Data" class | update,Data-method |
| Update method for the "DataDual" class | update,DataDual-method |
| Update method for the "DataParts" class | update,DataParts-method |
| Update method for the 'EffFlexi' Model class. This is a method to update estimates both for the flexible form model and the random walk model (see details in 'EffFlexi' class object) when new data or new observations of responses are available and added in. | update,EffFlexi-method |
| Update method for the 'Effloglog' Model class. This is a method to update the modal estimates of the model parameters theta_1 (theta1), theta_2 (theta2) and nu (nu, the precision of the efficacy responses) when new data or new observations of responses are available and added in. | update,Effloglog-method |
| Update method for the 'LogisticIndepBeta'Model class. This is a method to update the modal estimates of the model parameters phi_1 (phi1) and phi_2 (phi2) when new data or new observations of responses are available and added in. | update,LogisticIndepBeta-method |
| A Reference Class to help programming validation for new S4 classes | Validate |
| Creating a WinBUGS model file | writeModel |
