Package: crmPack 2.0.0.9001
Daniel Sabanes Bove
crmPack: Object-Oriented Implementation of CRM Designs
Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.
Authors:
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crmPack.pdf |crmPack.html✨
crmPack/json (API)
NEWS
| # Install 'crmPack' in R: |
| install.packages('crmPack', repos = c('https://openpharma.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/openpharma/crmpack/issues
Last updated 30 days agofrom:2e9413ddc3. Checks:ERROR: 3 WARNING: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | FAIL | Nov 15 2024 |
| R-4.5-win | WARNING | Nov 15 2024 |
| R-4.5-linux | WARNING | Nov 15 2024 |
| R-4.4-win | WARNING | Nov 15 2024 |
| R-4.4-mac | ERROR | Nov 15 2024 |
| R-4.3-win | WARNING | Nov 15 2024 |
| R-4.3-mac | ERROR | Nov 15 2024 |
Exports:.CohortSizeConst.CohortSizeDLT.CohortSizeMax.CohortSizeMin.CohortSizeOrdinal.CohortSizeParts.CohortSizeRange.CrmPackClass.DADesign.DALogisticLogNormal.DASimulations.Data.DataDA.DataDual.DataGrouped.DataMixture.DataOrdinal.DataParts.DefaultCohortSize.DefaultCohortSizeConst.DefaultCohortSizeDLT.DefaultCohortSizeMax.DefaultCohortSizeMin.DefaultCohortSizeOrdinal.DefaultCohortSizeParts.DefaultCohortSizeRange.DefaultDADesign.DefaultDALogisticLogNormal.DefaultDASimulations.DefaultData.DefaultDataDA.DefaultDataDual.DefaultDataGeneral.DefaultDataGrouped.DefaultDataMixture.DefaultDataOrdinal.DefaultDataParts.DefaultDesign.DefaultDesignGrouped.DefaultDesignOrdinal.DefaultDualDesign.DefaultDualEndpoint.DefaultDualEndpointBeta.DefaultDualEndpointEmax.DefaultDualEndpointRW.DefaultDualResponsesDesign.DefaultDualResponsesSamplesDesign.DefaultDualSimulations.DefaultDualSimulationsSummary.DefaultEffFlexi.DefaultEffloglog.DefaultFractionalCRM.DefaultGeneralModel.DefaultGeneralSimulations.DefaultGeneralSimulationsSummary.DefaultIncrements.DefaultIncrementsDoseLevels.DefaultIncrementsHSRBeta.DefaultIncrementsMaxToxProb.DefaultIncrementsMin.DefaultIncrementsOrdinal.DefaultIncrementsRelative.DefaultIncrementsRelativeDLT.DefaultIncrementsRelativeDLTCurrent.DefaultIncrementsRelativeParts.DefaultLogisticIndepBeta.DefaultLogisticKadane.DefaultLogisticKadaneBetaGamma.DefaultLogisticLogNormal.DefaultLogisticLogNormalGrouped.DefaultLogisticLogNormalMixture.DefaultLogisticLogNormalOrdinal.DefaultLogisticLogNormalSub.DefaultLogisticNormal.DefaultLogisticNormalFixedMixture.DefaultLogisticNormalMixture.DefaultMcmcOptions.DefaultModelEff.DefaultModelLogNormal.DefaultModelParamsNormal.DefaultModelPseudo.DefaultModelTox.DefaultNextBest.DefaultNextBestDualEndpoint.DefaultNextBestInfTheory.DefaultNextBestMaxGain.DefaultNextBestMaxGainSamples.DefaultNextBestMinDist.DefaultNextBestMTD.DefaultNextBestNCRM.DefaultNextBestNCRMLoss.DefaultNextBestOrdinal.DefaultNextBestProbMTDLTE.DefaultNextBestProbMTDMinDist.DefaultNextBestTD.DefaultNextBestTDsamples.DefaultNextBestThreePlusThree.DefaultOneParExpPrior.DefaultOneParLogNormalPrior.DefaultProbitLogNormal.DefaultProbitLogNormalRel.DefaultPseudoDualFlexiSimulations.DefaultPseudoDualSimulations.DefaultPseudoDualSimulationsSummary.DefaultPseudoSimulations.DefaultPseudoSimulationsSummary.DefaultRuleDesign.DefaultRuleDesignOrdinal.DefaultSafetyWindow.DefaultSafetyWindowConst.DefaultSafetyWindowSize.DefaultSamples.DefaultSimulations.DefaultSimulationsSummary.DefaultStoppingAll.DefaultStoppingAny.DefaultStoppingCohortsNearDose.DefaultStoppingExternal.DefaultStoppingHighestDose.DefaultStoppingList.DefaultStoppingLowestDoseHSRBeta.DefaultStoppingMaxGainCIRatio.DefaultStoppingMinCohorts.DefaultStoppingMinPatients.DefaultStoppingMissingDose.DefaultStoppingMTDCV.DefaultStoppingMTDdistribution.DefaultStoppingOrdinal.DefaultStoppingPatientsNearDose.DefaultStoppingSpecificDose.DefaultStoppingTargetBiomarker.DefaultStoppingTargetProb.DefaultStoppingTDCIRatio.DefaultTDDesign.DefaultTDsamplesDesign.DefaultTITELogisticLogNormal.Design.DesignGrouped.DesignOrdinal.DualDesign.DualEndpoint.DualEndpointBeta.DualEndpointEmax.DualEndpointRW.DualResponsesDesign.DualResponsesSamplesDesign.DualSimulations.DualSimulationsSummary.EffFlexi.Effloglog.FractionalCRM.GeneralData.GeneralModel.GeneralSimulations.GeneralSimulationsSummary.IncrementsDoseLevels.IncrementsHSRBeta.IncrementsMaxToxProb.IncrementsMin.IncrementsOrdinal.IncrementsRelative.IncrementsRelativeDLT.IncrementsRelativeDLTCurrent.IncrementsRelativeParts.LogisticIndepBeta.LogisticKadane.LogisticKadaneBetaGamma.LogisticLogNormal.LogisticLogNormalGrouped.LogisticLogNormalMixture.LogisticLogNormalOrdinal.LogisticLogNormalSub.LogisticNormal.LogisticNormalFixedMixture.LogisticNormalMixture.McmcOptions.ModelEff.ModelLogNormal.ModelParamsNormal.ModelPseudo.ModelTox.NextBestDualEndpoint.NextBestInfTheory.NextBestMaxGain.NextBestMaxGainSamples.NextBestMinDist.NextBestMTD.NextBestNCRM.NextBestNCRMLoss.NextBestOrdinal.NextBestProbMTDLTE.NextBestProbMTDMinDist.NextBestTD.NextBestTDsamples.NextBestThreePlusThree.OneParExpPrior.OneParLogNormalPrior.ProbitLogNormal.ProbitLogNormalRel.PseudoDualFlexiSimulations.PseudoDualSimulations.PseudoDualSimulationsSummary.PseudoSimulations.PseudoSimulationsSummary.RuleDesign.RuleDesignOrdinal.SafetyWindowConst.SafetyWindowSize.Samples.Simulations.SimulationsSummary.StoppingAll.StoppingAny.StoppingCohortsNearDose.StoppingExternal.StoppingHighestDose.StoppingList.StoppingLowestDoseHSRBeta.StoppingMaxGainCIRatio.StoppingMinCohorts.StoppingMinPatients.StoppingMissingDose.StoppingMTDCV.StoppingMTDdistribution.StoppingOrdinal.StoppingPatientsNearDose.StoppingSpecificDose.StoppingTargetBiomarker.StoppingTargetProb.StoppingTDCIRatio.TDDesign.TDsamplesDesign.TITELogisticLogNormal%>%approximateassert_equalassert_formatassert_lengthassert_probabilitiesassert_probabilityassert_probability_rangeassert_rangebiomarkercheck_equalcheck_formatcheck_lengthcheck_probabilitiescheck_probabilitycheck_probability_rangecheck_rangeCohortSizeConstCohortSizeDLTCohortSizeMaxCohortSizeMinCohortSizeOrdinalCohortSizePartsCohortSizeRangecrmPackExamplecrmPackHelpDADesignDALogisticLogNormaldapplyDASimulationsDataDataDADataDualDataGroupedDataMixtureDataOrdinalDataPartsDesignDesignGroupedDesignOrdinaldisable_loggingdosedose_grid_rangedoseFunctionDualDesignDualEndpointDualEndpointBetaDualEndpointEmaxDualEndpointRWDualResponsesDesignDualResponsesSamplesDesignDualSimulationsEffFlexiefficacyefficacyFunctionEffloglogenable_loggingexamineexpect_formatexpect_probabilitiesexpect_probabilityexpect_probability_rangeexpect_rangefitfitGainfitPEMFractionalCRMgainGeneralSimulationsgetgetEffh_all_equivalenth_check_fun_formalsh_convert_ordinal_datah_convert_ordinal_modelh_convert_ordinal_samplesh_default_if_emptyh_find_intervalh_format_numberh_in_rangeh_info_theory_disth_is_positive_definiteh_jags_add_dummyh_jags_extract_samplesh_jags_get_datah_jags_get_model_initsh_jags_join_modelsh_jags_write_modelh_model_dual_endpoint_betah_model_dual_endpoint_rhoh_model_dual_endpoint_sigma2betaWh_model_dual_endpoint_sigma2Wh_next_best_eligible_dosesh_next_best_mg_cih_next_best_mg_doses_at_gridh_next_best_mg_ploth_next_best_mgsamples_ploth_next_best_ncrm_loss_ploth_next_best_td_ploth_next_best_tdsamples_ploth_null_if_nah_rapplyh_slotsh_test_named_numerich_validate_combine_resultsIncrementsDoseLevelsIncrementsHSRBetaIncrementsMaxToxProbIncrementsMinIncrementsOrdinalIncrementsRelativeIncrementsRelativeDLTIncrementsRelativeDLTCurrentIncrementsRelativePartsis_logging_enabledlog_traceLogisticIndepBetaLogisticKadaneLogisticKadaneBetaGammaLogisticLogNormalLogisticLogNormalGroupedLogisticLogNormalMixtureLogisticLogNormalOrdinalLogisticLogNormalSubLogisticNormalLogisticNormalFixedMixtureLogisticNormalMixturelogitmatch_within_tolerancemaxDosemaxSizemcmcMcmcOptionsMinimalInformativeminSizeModelLogNormalModelParamsNormalnextBestNextBestDualEndpointNextBestInfTheoryNextBestMaxGainNextBestMaxGainSamplesNextBestMinDistNextBestMTDNextBestNCRMNextBestNCRMLossNextBestOrdinalNextBestProbMTDLTENextBestProbMTDMinDistNextBestTDNextBestTDsamplesNextBestThreePlusThreengridOneParExpPriorOneParLogNormalPriorplotplotDualResponsesplotGainprobprobFunctionprobitProbitLogNormalProbitLogNormalRelPseudoDualSimulationsPseudoSimulationsQuantiles2LogisticNormalRuleDesignRuleDesignOrdinalSafetyWindowConstSafetyWindowSizeSamplessaveSampleset_seedshowsimulateSimulationssizeStoppingAllStoppingAnyStoppingCohortsNearDoseStoppingExternalStoppingHighestDoseStoppingListStoppingLowestDoseHSRBetaStoppingMaxGainCIRatioStoppingMinCohortsStoppingMinPatientsStoppingMissingDoseStoppingMTDCVStoppingMTDdistributionStoppingOrdinalStoppingPatientsNearDoseStoppingSpecificDoseStoppingTargetBiomarkerStoppingTargetProbStoppingTDCIRatiostopTrialsummaryTDDesignTDsamplesDesigntest_formattest_lengthtest_probabilitiestest_probabilitytest_probability_rangetest_rangeThreePlusThreeDesigntidyTITELogisticLogNormalupdatewindowLength
Dependencies:backportsbase64encbslibcachemcheckmateclicodacolorspacecpp11digestdplyrevaluatefansifarverfastmapfontawesomeformatRfsfutile.loggerfutile.optionsgenericsGenSAggplot2gluegridExtragtablehighrhtmltoolsisobandjquerylibjsonlitekableExtraknitrlabelinglambda.rlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellmvtnormnlmeparallellypillarpkgconfigR6rappdirsRColorBrewerrjagsrlangrmarkdownrstudioapisassscalesstringistringrsurvivalsvglitesystemfontstibbletidyselecttinytexutf8vctrsviridisLitewithrxfunxml2yaml
Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Object-oriented implementation of CRM designs | -package crmPack-package crmPack |
| 'CohortSize' | .DefaultCohortSize CohortSize CohortSize-class |
| 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 |
| Approximate posterior with (log) normal distribution | approximate approximate,Samples-method |
| Additional Assertions for 'checkmate' | assertions |
| Get the Biomarker Levels for a Given Dual-Endpoint Model, Given Dose Levels and Samples | biomarker biomarker,integer,DualEndpoint,Samples-method biomarker-DualEndpoint |
| Check if All Arguments Are Equal | assert_equal check_equal |
| Check that an argument is a valid format specification | assert_format check_format expect_format test_format |
| Check if vectors are of compatible lengths | assert_length check_length test_length |
| Check if an argument is a probability vector | assert_probabilities check_probabilities expect_probabilities test_probabilities |
| Check if an argument is a single probability value | assert_probability check_probability expect_probability test_probability |
| Check if an argument is a probability range | assert_probability_range check_probability_range expect_probability_range test_probability_range |
| Check that an argument is a numerical range | assert_range check_range expect_range test_range |
| 'CohortSizeConst' | .CohortSizeConst .DefaultCohortSizeConst CohortSizeConst CohortSizeConst-class |
| 'CohortSizeDLT' | .CohortSizeDLT .DefaultCohortSizeDLT CohortSizeDLT CohortSizeDLT-class |
| 'CohortSizeMax' | .CohortSizeMax .DefaultCohortSizeMax CohortSizeMax CohortSizeMax-class |
| 'CohortSizeMin' | .CohortSizeMin .DefaultCohortSizeMin CohortSizeMin CohortSizeMin-class |
| 'CohortSizeOrdinal' | .CohortSizeOrdinal .DefaultCohortSizeOrdinal CohortSizeOrdinal CohortSizeOrdinal-class |
| 'CohortSizeParts' | .CohortSizeParts .DefaultCohortSizeParts CohortSizeParts CohortSizeParts-class |
| 'CohortSizeRange' | .CohortSizeRange .DefaultCohortSizeRange CohortSizeRange CohortSizeRange-class |
| 'CrmPackClass' | .CrmPackClass CrmPackClass CrmPackClass-class |
| Open the example pdf for crmPack | crmPackExample |
| Open the browser with help pages for crmPack | crmPackHelp |
| 'DADesign' | .DADesign .DefaultDADesign DADesign DADesign-class |
| 'DALogisticLogNormal' | .DALogisticLogNormal .DefaultDALogisticLogNormal DALogisticLogNormal DALogisticLogNormal-class |
| Apply a Function to Subsets of Data Frame. | dapply |
| Initialization function for 'DASimulations' | DASimulations |
| Class for the simulations output from DA based designs | .DASimulations .DefaultDASimulations DASimulations-class |
| 'Data' | .Data .DefaultData Data Data-class |
| 'DataDA' | .DataDA .DefaultDataDA DataDA DataDA-class |
| 'DataDual' | .DataDual .DefaultDataDual DataDual DataDual-class |
| 'DataGrouped' | .DataGrouped .DefaultDataGrouped DataGrouped DataGrouped-class |
| 'DataMixture' | .DataMixture .DefaultDataMixture DataMixture DataMixture-class |
| 'DataOrdinal' | .DataOrdinal .DefaultDataOrdinal DataOrdinal DataOrdinal-class |
| 'DataParts' | .DataParts .DefaultDataParts DataParts DataParts-class |
| 'Design' | .DefaultDesign .Design Design Design-class |
| 'DesignGrouped' | .DefaultDesignGrouped .DesignGrouped DesignGrouped DesignGrouped-class |
| 'DesignOrdinal' | .DefaultDesignOrdinal .DesignOrdinal DesignOrdinal DesignOrdinal-class |
| Computing the Doses for a given independent variable, Model and Samples | dose dose,numeric,DualEndpoint,Samples-method dose,numeric,EffFlexi,Samples-method dose,numeric,Effloglog,missing-method dose,numeric,LogisticIndepBeta,missing-method dose,numeric,LogisticIndepBeta,Samples-method dose,numeric,LogisticKadane,Samples-method dose,numeric,LogisticKadaneBetaGamma,Samples-method dose,numeric,LogisticLogNormal,Samples-method dose,numeric,LogisticLogNormalGrouped,Samples-method dose,numeric,LogisticLogNormalMixture,Samples-method dose,numeric,LogisticLogNormalOrdinal,Samples-method dose,numeric,LogisticLogNormalSub,Samples-method dose,numeric,LogisticNormal,Samples-method dose,numeric,LogisticNormalFixedMixture,Samples-method dose,numeric,LogisticNormalMixture,Samples-method dose,numeric,OneParExpPrior,Samples-method dose,numeric,OneParLogNormalPrior,Samples-method dose,numeric,ProbitLogNormal,Samples-method dose,numeric,ProbitLogNormalRel,Samples-method dose-DualEndpoint dose-EffFlexi dose-Effloglog-noSamples dose-LogisticIndepBeta dose-LogisticIndepBeta-noSamples dose-LogisticKadane dose-LogisticKadaneBetaGamma dose-LogisticLogNormal dose-LogisticLogNormalGrouped dose-LogisticLogNormalMixture dose-LogisticLogNormalOrdinal dose-LogisticLogNormalSub dose-LogisticNormal dose-LogisticNormalFixedMixture dose-LogisticNormalMixture dose-OneParExpPrior dose-OneParLogNormalPrior dose-ProbitLogNormal dose-ProbitLogNormalRel |
| Getting the Dose Grid Range | dose_grid_range dose_grid_range,Data-method dose_grid_range,DataOrdinal-method dose_grid_range-Data |
| Getting the Dose Function for a Given Model Type | doseFunction doseFunction,GeneralModel-method doseFunction,LogisticLogNormalOrdinal-method doseFunction,ModelPseudo-method doseFunction-GeneralModel doseFunction-LogisticLogNormalOrdinal doseFunction-ModelPseudo |
| 'DualDesign' | .DefaultDualDesign .DualDesign DualDesign DualDesign-class |
| 'DualEndpoint' | .DefaultDualEndpoint .DualEndpoint DualEndpoint DualEndpoint-class |
| 'DualEndpointBeta' | .DefaultDualEndpointBeta .DualEndpointBeta DualEndpointBeta DualEndpointBeta-class |
| 'DualEndpointEmax' | .DefaultDualEndpointEmax .DualEndpointEmax DualEndpointEmax DualEndpointEmax-class |
| 'DualEndpointRW' | .DefaultDualEndpointRW .DualEndpointRW DualEndpointRW DualEndpointRW-class |
| 'DualResponsesDesign.R' | .DefaultDualResponsesDesign .DualResponsesDesign DualResponsesDesign DualResponsesDesign-class |
| 'DualResponsesSamplesDesign' | .DefaultDualResponsesSamplesDesign .DualResponsesSamplesDesign DualResponsesSamplesDesign DualResponsesSamplesDesign-class |
| 'DualSimulations' | .DefaultDualSimulations .DualSimulations DualSimulations DualSimulations-class |
| 'DualSimulationsSummary' | .DefaultDualSimulationsSummary .DualSimulationsSummary DualSimulationsSummary DualSimulationsSummary-class |
| 'EffFlexi' | .DefaultEffFlexi .EffFlexi EffFlexi EffFlexi-class |
| Computing Expected Efficacy for a Given Dose, Model and Samples | efficacy efficacy,numeric,EffFlexi,Samples-method efficacy,numeric,Effloglog,missing-method efficacy,numeric,Effloglog,Samples-method efficacy-EffFlexi efficacy-Effloglog efficacy-Effloglog-noSamples |
| Getting the Efficacy Function for a Given Model Type | efficacyFunction efficacyFunction,ModelEff-method efficacyFunction-ModelEff |
| 'Effloglog' | .DefaultEffloglog .Effloglog Effloglog Effloglog-class |
| Verbose Logging | disable_logging enable_logging is_logging_enabled log_trace |
| Obtain hypothetical trial course table for a design | examine examine,DADesign-method examine,Design-method examine,RuleDesign-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,GeneralModel,Data-method fit,Samples,LogisticIndepBeta,Data-method fit,Samples,LogisticLogNormalOrdinal,DataOrdinal-method |
| Get the fitted 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 |
| Get the fitted DLT free survival (piecewise exponential model). This function returns a data frame with dose, middle, lower and upper quantiles for the 'PEM' curve. If hazard=TRUE, | fitPEM fitPEM,Samples,DALogisticLogNormal,DataDA-method |
| 'FractionalCRM' | .DefaultFractionalCRM .FractionalCRM FractionalCRM FractionalCRM-class |
| Compute Gain Values based on Pseudo DLE and a Pseudo Efficacy Models and Using Optional Samples. | gain gain,numeric,ModelTox,missing,Effloglog,missing-method gain,numeric,ModelTox,Samples,ModelEff,Samples-method gain-ModelTox-Effloglog-noSamples gain-ModelTox-ModelEff |
| 'GeneralData' | .DefaultDataGeneral .GeneralData GeneralData GeneralData-class |
| 'GeneralModel' | .DefaultGeneralModel .GeneralModel GeneralModel GeneralModel-class |
| 'GeneralSimulations' @description *[Stable]* This class captures trial simulations. Here also the random generator state before starting the simulation is saved, in order to be able to reproduce the outcome. For this just use 'set.seed' with the 'seed' as argument before running 'simulate,Design-method'. | .DefaultGeneralSimulations .GeneralSimulations GeneralSimulations GeneralSimulations-class |
| 'GeneralSimulationsSummary' | .DefaultGeneralSimulationsSummary .DefaultPseudoSimulationsSummary .GeneralSimulationsSummary GeneralSimulationsSummary GeneralSimulationsSummary-class |
| Get specific parameter samples and produce a data.frame | get,Samples,character-method |
| Extracting Efficacy Responses for Subjects Categorized by the DLT | getEff getEff,DataDual-method getEff-DataDual |
| Comparison with Numerical Tolerance and Without Name Comparison | h_all_equivalent |
| Helper Function to Blind Plot Data | h_blind_plot_data |
| Helper function to calculate percentage of true stopping rules for report label output calculates true column means and converts output into percentages before combining the output with the report label; output is passed to 'show()' and output with cat to console | h_calc_report_label_percentage |
| Checking Formals of a Function | h_check_fun_formals |
| Convert a Ordinal Data to the Equivalent Binary Data for a Specific Grade | h_convert_ordinal_data |
| Convert an ordinal CRM model to the Equivalent Binary CRM Model for a Specific Grade | h_convert_ordinal_model |
| Convert a Samples Object from an ordinal Model to the Equivalent Samples Object from a Binary Model | h_convert_ordinal_samples |
| Getting the default value for an empty object | h_default_if_empty |
| Find Interval Numbers or Indices and Return Custom Number For 0. | h_find_interval |
| Conditional Formatting Using C-style Formats | h_format_number |
| Check which elements are in a given range | h_in_range |
| Calculating the Information Theoretic Distance | h_info_theory_dist |
| Testing Matrix for Positive Definiteness | h_is_positive_definite |
| Appending a Dummy Number for Selected Slots in Data | h_jags_add_dummy |
| Extracting Samples from 'JAGS' 'mcarray' Object | h_jags_extract_samples |
| Getting Data for 'JAGS' | h_jags_get_data |
| Setting Initial Values for 'JAGS' Model Parameters | h_jags_get_model_inits |
| Joining 'JAGS' Models | h_jags_join_models |
| Writing JAGS Model to a File | h_jags_write_model |
| Update certain components of 'DualEndpoint' model with regard to parameters of the function that models dose-biomarker relationship defined in the 'DualEndpointBeta' class. | h_model_dual_endpoint_beta |
| Update 'DualEndpoint' class model components with regard to DLT and biomarker correlation. | h_model_dual_endpoint_rho |
| Update certain components of 'DualEndpoint' model with regard to prior variance factor of the random walk. | h_model_dual_endpoint_sigma2betaW |
| Update 'DualEndpoint' class model components with regard to biomarker regression variance. | h_model_dual_endpoint_sigma2W |
| Get Eligible Doses from the Dose Grid. | h_next_best_eligible_doses |
| Credibility Intervals for Max Gain and Target Doses at 'nextBest-NextBestMaxGain' Method. | h_next_best_mg_ci |
| Get Closest Grid Doses for a Given Target Doses for 'nextBest-NextBestMaxGain' Method. | h_next_best_mg_doses_at_grid |
| Building the Plot for 'nextBest-NextBestMaxGain' Method. | h_next_best_mg_plot |
| Building the Plot for 'nextBest-NextBestMaxGainSamples' Method. | h_next_best_mgsamples_plot |
| Building the Plot for 'nextBest-NextBestNCRMLoss' Method. | h_next_best_ncrm_loss_plot |
| Building the Plot for 'nextBest-NextBestTD' Method. | h_next_best_td_plot |
| Building the Plot for 'nextBest-NextBestTDsamples' Method. | h_next_best_tdsamples_plot |
| Getting 'NULL' for 'NA' | h_null_if_na |
| Helper Function Containing Common Functionality | h_obtain_dose_grid_range |
| Preparing Cohort Lines for Data Plot | h_plot_data_cohort_lines |
| Helper Function for the Plot Method of the Data and DataOrdinal Classes | h_plot_data_dataordinal plot,Data,missing-method plot,DataOrdinal,missing-method plot-Data |
| Preparing Data for Plotting | h_plot_data_df h_plot_data_df,Data-method h_plot_data_df,DataOrdinal-method |
| Recursively Apply a Function to a List | h_rapply |
| Getting the Slots from a S4 Object | h_slots |
| Helper function to calculate average across iterations for each additional reporting parameter extracts parameter names as specified by user and averaged the values for each specified parameter to 'show()' and output with cat to console | h_summarize_add_stats |
| Check that an argument is a named vector of type numeric | h_test_named_numeric |
| Helper function to recursively unpack stopping rules and return lists with logical value and label given | h_unpack_stopit |
| Combining S4 Class Validation Results | h_validate_combine_results |
| Helper Function performing validation Common to Data and DataOrdinal | h_validate_common_data_slots |
| 'Increments' | .DefaultIncrements Increments Increments-class |
| 'IncrementsDoseLevels' | .DefaultIncrementsDoseLevels .IncrementsDoseLevels IncrementsDoseLevels IncrementsDoseLevels-class |
| 'IncrementsHSRBeta' | .DefaultIncrementsHSRBeta .IncrementsHSRBeta IncrementsHSRBeta IncrementsHSRBeta-class |
| 'IncrementsMaxToxProb' | .DefaultIncrementsMaxToxProb .IncrementsMaxToxProb IncrementsMaxToxProb IncrementsMaxToxProb-class |
| 'IncrementsMin' | .DefaultIncrementsMin .IncrementsMin IncrementsMin IncrementsMin-class |
| 'IncrementsOrdinal' | .DefaultIncrementsOrdinal .IncrementsOrdinal IncrementsOrdinal IncrementsOrdinal-class |
| 'IncrementsRelative' | .DefaultIncrementsRelative .IncrementsRelative IncrementsRelative IncrementsRelative-class |
| 'IncrementsRelativeDLT' | .DefaultIncrementsRelativeDLT .IncrementsRelativeDLT IncrementsRelativeDLT IncrementsRelativeDLT-class |
| 'IncrementsRelativeDLTCurrent' | .DefaultIncrementsRelativeDLTCurrent .IncrementsRelativeDLTCurrent IncrementsRelativeDLTCurrent IncrementsRelativeDLTCurrent-class |
| 'IncrementsRelativeParts' | .DefaultIncrementsRelativeParts .IncrementsRelativeParts IncrementsRelativeParts IncrementsRelativeParts-class |
| Render a 'CohortSizeConst' Object | knit_print knit_print.CohortSizeConst knit_print.CohortSizeDLT knit_print.CohortSizeMax knit_print.CohortSizeMin knit_print.CohortSizeOrdinal knit_print.CohortSizeParts knit_print.CohortSizeRange knit_print.DADesign knit_print.DataParts knit_print.Design knit_print.DesignGrouped knit_print.DesignOrdinal knit_print.DualDesign knit_print.DualEndpoint knit_print.DualResponsesDesign knit_print.DualResponsesSamplesDesign knit_print.Effloglog knit_print.GeneralData knit_print.GeneralModel knit_print.IncrementsDoseLevels knit_print.IncrementsHSRBeta knit_print.IncrementsMin knit_print.IncrementsOrdinal knit_print.IncrementsRelative knit_print.IncrementsRelativeDLT knit_print.IncrementsRelativeDLTCurrent knit_print.IncrementsRelativeParts knit_print.LogisticIndepBeta knit_print.LogisticKadane knit_print.LogisticKadaneBetaGamma knit_print.LogisticLogNormal knit_print.LogisticLogNormalGrouped knit_print.LogisticLogNormalMixture knit_print.LogisticLogNormalOrdinal knit_print.LogisticLogNormalSub knit_print.LogisticNormalFixedMixture knit_print.LogisticNormalMixture knit_print.ModelParamsNormal knit_print.NextBestDualEndpoint knit_print.NextBestInfTheory knit_print.NextBestMaxGain knit_print.NextBestMaxGainSamples knit_print.NextBestMinDist knit_print.NextBestMTD knit_print.NextBestNCRM knit_print.NextBestNCRMLoss knit_print.NextBestOrdinal knit_print.NextBestProbMTDLTE knit_print.NextBestProbMTDMinDist knit_print.NextBestTD knit_print.NextBestTDsamples knit_print.NextBestThreePlusThree knit_print.OneParExpPrior knit_print.OneParLogNormalPrior knit_print.RuleDesign knit_print.RuleDesignOrdinal knit_print.SafetyWindow knit_print.SafetyWindowConst knit_print.SafetyWindowSize knit_print.StartingDose knit_print.StoppingAll knit_print.StoppingAny knit_print.StoppingCohortsNearDose knit_print.StoppingHighestDose knit_print.StoppingList knit_print.StoppingLowestDoseHSRBeta knit_print.StoppingMaxGainCIRatio knit_print.StoppingMinCohorts knit_print.StoppingMinPatients knit_print.StoppingMissingDose knit_print.StoppingMTDCV knit_print.StoppingMTDdistribution knit_print.StoppingOrdinal knit_print.StoppingPatientsNearDose knit_print.StoppingSpecificDose knit_print.StoppingTargetBiomarker knit_print.StoppingTargetProb knit_print.StoppingTDCIRatio knit_print.TDDesign knit_print.TDsamplesDesign |
| 'LogisticIndepBeta' | .DefaultLogisticIndepBeta .LogisticIndepBeta LogisticIndepBeta LogisticIndepBeta-class |
| 'LogisticKadane' | .DefaultLogisticKadane .LogisticKadane LogisticKadane LogisticKadane-class |
| 'LogisticKadaneBetaGamma' | .DefaultLogisticKadaneBetaGamma .LogisticKadaneBetaGamma LogisticKadaneBetaGamma LogisticKadaneBetaGamma-class |
| 'LogisticLogNormal' | .DefaultLogisticLogNormal .LogisticLogNormal LogisticLogNormal LogisticLogNormal-class |
| 'LogisticLogNormalGrouped' | .DefaultLogisticLogNormalGrouped .LogisticLogNormalGrouped LogisticLogNormalGrouped LogisticLogNormalGrouped-class |
| 'LogisticLogNormalMixture' | .DefaultLogisticLogNormalMixture .LogisticLogNormalMixture LogisticLogNormalMixture LogisticLogNormalMixture-class |
| 'LogisticLogNormalOrdinal' | .DefaultLogisticLogNormalOrdinal .LogisticLogNormalOrdinal LogisticLogNormalOrdinal LogisticLogNormalOrdinal-class |
| 'LogisticLogNormalSub' | .DefaultLogisticLogNormalSub .LogisticLogNormalSub LogisticLogNormalSub LogisticLogNormalSub-class |
| 'LogisticNormal' | .DefaultLogisticNormal .LogisticNormal LogisticNormal LogisticNormal-class |
| 'LogisticNormalFixedMixture' | .DefaultLogisticNormalFixedMixture .LogisticNormalFixedMixture LogisticNormalFixedMixture LogisticNormalFixedMixture-class |
| 'LogisticNormalMixture' | .DefaultLogisticNormalMixture .LogisticNormalMixture LogisticNormalMixture LogisticNormalMixture-class |
| Shorthand for logit function | logit |
| Helper function for value matching with tolerance | match_within_tolerance |
| Determine the Maximum Possible Next Dose | maxDose maxDose,IncrementsDoseLevels,Data-method maxDose,IncrementsHSRBeta,Data-method maxDose,IncrementsMaxToxProb,Data-method maxDose,IncrementsMaxToxProb,DataOrdinal-method maxDose,IncrementsMin,Data-method maxDose,IncrementsMin,DataOrdinal-method maxDose,IncrementsOrdinal,DataOrdinal-method maxDose,IncrementsRelative,Data-method maxDose,IncrementsRelativeDLT,Data-method maxDose,IncrementsRelativeDLTCurrent,Data-method maxDose,IncrementsRelativeParts,DataParts-method maxDose-IncrementsDoseLevels maxDose-IncrementsHSRBeta maxDose-IncrementsMaxToxProb maxDose-IncrementsMin maxDose-IncrementsOrdinal maxDose-IncrementsRelative maxDose-IncrementsRelativeDLT maxDose-IncrementsRelativeDLTCurrent maxDose-IncrementsRelativeParts |
| "MAX" combination of cohort size rules | maxSize maxSize,CohortSize-method |
| Obtaining 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,DataOrdinal,LogisticLogNormalOrdinal,McmcOptions-method mcmc,GeneralData,DualEndpointBeta,McmcOptions-method mcmc,GeneralData,DualEndpointEmax,McmcOptions-method mcmc,GeneralData,DualEndpointRW,McmcOptions-method mcmc,GeneralData,GeneralModel,McmcOptions-method mcmc,GeneralData,OneParExpPrior,McmcOptions-method mcmc,GeneralData,OneParLogNormalPrior,McmcOptions-method mcmc-GeneralData mcmc-GeneralData-DualEndpointBeta mcmc-GeneralData-DualEndpointEmax mcmc-GeneralData-DualEndpointRW mcmc-GeneralData-OneParExpPrior mcmc-GeneralData-OneParLogNormalPrior |
| 'McmcOptions' | .DefaultMcmcOptions .McmcOptions McmcOptions McmcOptions-class |
| Construct a minimally informative prior | MinimalInformative |
| "MIN" combination of cohort size rules | minSize minSize,CohortSize-method |
| 'ModelEff' | .DefaultModelEff .ModelEff ModelEff ModelEff-class |
| 'ModelLogNormal' | .DefaultModelLogNormal .ModelLogNormal ModelLogNormal ModelLogNormal-class |
| 'ModelParamsNormal' | .DefaultModelParamsNormal .ModelParamsNormal ModelParamsNormal ModelParamsNormal-class |
| 'ModelPseudo' | .DefaultModelPseudo .ModelPseudo ModelPseudo ModelPseudo-class |
| 'ModelTox' | .DefaultModelTox .ModelTox ModelTox ModelTox-class |
| The Names of the Sampled Parameters | names,Samples-method names-Samples |
| Finding the Next Best Dose | nextBest nextBest,NextBestDualEndpoint,numeric,Samples,DualEndpoint,Data-method nextBest,NextBestInfTheory,numeric,Samples,GeneralModel,Data-method nextBest,NextBestMaxGain,numeric,missing,ModelTox,DataDual-method nextBest,NextBestMaxGainSamples,numeric,Samples,ModelTox,DataDual-method nextBest,NextBestMinDist,numeric,Samples,GeneralModel,Data-method nextBest,NextBestMTD,numeric,Samples,GeneralModel,Data-method nextBest,NextBestNCRM,numeric,Samples,GeneralModel,Data-method nextBest,NextBestNCRM,numeric,Samples,GeneralModel,DataParts-method nextBest,NextBestNCRMLoss,numeric,Samples,GeneralModel,Data-method nextBest,NextBestOrdinal,numeric,Samples,GeneralModel,Data-method nextBest,NextBestOrdinal,numeric,Samples,LogisticLogNormalOrdinal,DataOrdinal-method nextBest,NextBestProbMTDLTE,numeric,Samples,GeneralModel,Data-method nextBest,NextBestProbMTDMinDist,numeric,Samples,GeneralModel,Data-method nextBest,NextBestTD,numeric,missing,LogisticIndepBeta,Data-method nextBest,NextBestTDsamples,numeric,Samples,LogisticIndepBeta,Data-method nextBest,NextBestThreePlusThree,missing,missing,missing,Data-method nextBest-NextBestDualEndpoint nextBest-NextBestInfTheory nextBest-NextBestMaxGain nextBest-NextBestMaxGainSamples nextBest-NextBestMinDist nextBest-NextBestMTD nextBest-NextBestNCRM nextBest-NextBestNCRM-DataParts nextBest-NextBestNCRMLoss nextBest-NextBestOrdinal nextBest-NextBestProbMTDLTE nextBest-NextBestProbMTDMinDist nextBest-NextBestTD nextBest-NextBestTDsamples nextBest-NextBestThreePlusThree |
| 'NextBest' | .DefaultNextBest NextBest NextBest-class |
| 'NextBestDualEndpoint' | .DefaultNextBestDualEndpoint .NextBestDualEndpoint NextBestDualEndpoint NextBestDualEndpoint-class |
| 'NextBestInfTheory' | .DefaultNextBestInfTheory .NextBestInfTheory NextBestInfTheory NextBestInfTheory-class |
| 'NextBestMaxGain' | .DefaultNextBestMaxGain .NextBestMaxGain NextBestMaxGain NextBestMaxGain-class |
| 'NextBestMaxGainSamples' | .DefaultNextBestMaxGainSamples .NextBestMaxGainSamples NextBestMaxGainSamples NextBestMaxGainSamples-class |
| 'NextBestMinDist' | .DefaultNextBestMinDist .NextBestMinDist NextBestMinDist NextBestMinDist-class |
| 'NextBestMTD' | .DefaultNextBestMTD .NextBestMTD NextBestMTD NextBestMTD-class |
| 'NextBestNCRM' | .DefaultNextBestNCRM .NextBestNCRM NextBestNCRM NextBestNCRM-class |
| 'NextBestNCRMLoss' | .DefaultNextBestNCRMLoss .NextBestNCRMLoss NextBestNCRMLoss NextBestNCRMLoss-class |
| 'NextBestOrdinal' | .DefaultNextBestOrdinal .NextBestOrdinal NextBestOrdinal NextBestOrdinal-class |
| 'NextBestProbMTDLTE' | .DefaultNextBestProbMTDLTE .NextBestProbMTDLTE NextBestProbMTDLTE NextBestProbMTDLTE-class |
| 'NextBestProbMTDMinDist' | .DefaultNextBestProbMTDMinDist .NextBestProbMTDMinDist NextBestProbMTDMinDist NextBestProbMTDMinDist-class |
| 'NextBestTD' | .DefaultNextBestTD .NextBestTD NextBestTD NextBestTD-class |
| 'NextBestTDsamples' | .DefaultNextBestTDsamples .NextBestTDsamples NextBestTDsamples NextBestTDsamples-class |
| 'NextBestThreePlusThree' | .DefaultNextBestThreePlusThree .NextBestThreePlusThree NextBestThreePlusThree NextBestThreePlusThree-class |
| Number of Doses in Grid | ngrid ngrid,Data-method ngrid-Data |
| 'OneParExpPrior' | .DefaultOneParExpPrior .OneParExpPrior OneParExpPrior OneParExpPrior-class |
| 'OneParLogNormalPrior' | .DefaultOneParLogNormalPrior .OneParLogNormalPrior OneParLogNormalPrior OneParLogNormalPrior-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 of the fitted dose-tox based with a given pseudo DLE model and data without samples | plot,Data,ModelTox-method |
| Plot Method for the 'DataDA' Class | plot,DataDA,missing-method plot-DataDA |
| Plot Method for the 'DataDual' Class | plot,DataDual,missing-method plot-DataDual |
| 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 |
| This plot method can be applied to 'PseudoDualFlexiSimulations' objects in order to summarize them graphically. Possible 'type's of plots at the moment are: trajectory Summary of the trajectory of the simulated trials dosesTried Average proportions of the doses tested in patients sigma2 The variance of the efficacy responses sigma2betaW The variance of the random walk model You can specify one or both of these in the 'type' argument. | 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 model fits | plot,Samples,DALogisticLogNormal-method |
| Plotting dose-toxicity and dose-biomarker model fits | plot,Samples,DualEndpoint-method |
| Plotting dose-toxicity model fits | plot,Samples,GeneralModel-method |
| Plot the fitted dose-efficacy 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 |
| Plot 'gtable' Objects | plot.gtable print.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 |
| 'positive_number' | positive_number |
| Computing Toxicity Probabilities for a Given Dose, Model and Samples | prob prob,numeric,DualEndpoint,Samples-method prob,numeric,LogisticIndepBeta,missing-method prob,numeric,LogisticIndepBeta,Samples-method prob,numeric,LogisticKadane,Samples-method prob,numeric,LogisticKadaneBetaGamma,Samples-method prob,numeric,LogisticLogNormal,Samples-method prob,numeric,LogisticLogNormalGrouped,Samples-method prob,numeric,LogisticLogNormalMixture,Samples-method prob,numeric,LogisticLogNormalOrdinal,Samples-method prob,numeric,LogisticLogNormalSub,Samples-method prob,numeric,LogisticNormal,Samples-method prob,numeric,LogisticNormalFixedMixture,Samples-method prob,numeric,LogisticNormalMixture,Samples-method prob,numeric,OneParExpPrior,Samples-method prob,numeric,OneParLogNormalPrior,Samples-method prob,numeric,ProbitLogNormal,Samples-method prob,numeric,ProbitLogNormalRel,Samples-method prob-DualEndpoint prob-LogisticIndepBeta prob-LogisticIndepBeta-noSamples prob-LogisticKadane prob-LogisticKadaneBetaGamma prob-LogisticLogNormal prob-LogisticLogNormalGrouped prob-LogisticLogNormalMixture prob-LogisticLogNormalOrdinal prob-LogisticLogNormalSub prob-LogisticNormal prob-LogisticNormalFixedMixture prob-LogisticNormalMixture prob-OneParExpPrior prob-OneParLogNormalPrior prob-ProbitLogNormal prob-ProbitLogNormalRel |
| Getting the Prob Function for a Given Model Type | probFunction probFunction,GeneralModel-method probFunction,LogisticLogNormalOrdinal-method probFunction,ModelTox-method probFunction-GeneralModel probFunction-LogisticLogNormalOrdinal probFunction-ModelTox |
| Shorthand for probit function | probit |
| 'ProbitLogNormal' | .DefaultProbitLogNormal .ProbitLogNormal ProbitLogNormal ProbitLogNormal-class ProbitLogNormalLogDose |
| 'ProbitLogNormalRel' | .DefaultProbitLogNormalRel .ProbitLogNormalRel ProbitLogNormalRel ProbitLogNormalRel-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. | .DefaultPseudoDualFlexiSimulations .PseudoDualFlexiSimulations PseudoDualFlexiSimulations-class |
| 'PseudoDualSimulations' | .DefaultPseudoDualSimulations .PseudoDualSimulations PseudoDualSimulations PseudoDualSimulations-class |
| Class for the summary of the dual responses simulations using pseudo models | .DefaultPseudoDualSimulationsSummary .PseudoDualSimulationsSummary PseudoDualSimulationsSummary-class |
| 'PseudoSimulations' | .DefaultPseudoSimulations .PseudoSimulations 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 |
| 'RuleDesign' | .DefaultRuleDesign .RuleDesign RuleDesign RuleDesign-class ThreePlusThreeDesign |
| 'RuleDesignOrdinal' | .DefaultRuleDesignOrdinal .RuleDesignOrdinal RuleDesignOrdinal RuleDesignOrdinal-class |
| 'SafetyWindow' | .DefaultSafetyWindow SafetyWindow SafetyWindow-class |
| 'SafetyWindowConst' | .DefaultSafetyWindowConst .SafetyWindowConst SafetyWindowConst SafetyWindowConst-class |
| 'SafetyWindowSize' | .DefaultSafetyWindowSize .SafetyWindowSize SafetyWindowSize SafetyWindowSize-class |
| 'Samples' | .DefaultSamples .Samples Samples Samples-class |
| Determining if this Sample Should be Saved | saveSample saveSample,McmcOptions-method saveSample-McmcOptions |
| Helper Function to Set and Save the RNG Seed | set_seed |
| 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 time-to-DLT augmented CRM design ('DADesign') | simulate,DADesign-method |
| Simulate outcomes from a CRM design | simulate,Design-method |
| Simulate Method for the 'DesignGrouped' Class | simulate,DesignGrouped-method simulate-DesignGrouped |
| 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 |
| 'Simulations' | .DefaultSimulations .Simulations Simulations Simulations-class |
| 'SimulationsSummary' | .DefaultSimulationsSummary .SimulationsSummary SimulationsSummary SimulationsSummary-class |
| Size of an Object | size size,CohortSizeConst-method size,CohortSizeDLT-method size,CohortSizeMax-method size,CohortSizeMin-method size,CohortSizeOrdinal-method size,CohortSizeParts-method size,CohortSizeRange-method size,McmcOptions-method size,Samples-method size-CohortSizeConst size-CohortSizeDLT size-CohortSizeMax size-CohortSizeMin size-CohortSizeOrdinal size-CohortSizeParts size-CohortSizeRange size-McmcOptions size-Samples |
| 'Stopping' | Stopping Stopping-class |
| 'StoppingAll' | .DefaultStoppingAll .StoppingAll StoppingAll StoppingAll-class |
| 'StoppingAny' | .DefaultStoppingAny .StoppingAny StoppingAny StoppingAny-class |
| 'StoppingCohortsNearDose' | .DefaultStoppingCohortsNearDose .StoppingCohortsNearDose StoppingCohortsNearDose StoppingCohortsNearDose-class |
| 'StoppingExternal' | .DefaultStoppingExternal .StoppingExternal StoppingExternal StoppingExternal-class |
| 'StoppingHighestDose' | .DefaultStoppingHighestDose .StoppingHighestDose StoppingHighestDose StoppingHighestDose-class |
| 'StoppingList' | .DefaultStoppingList .StoppingList StoppingList StoppingList-class |
| 'StoppingLowestDoseHSRBeta' | .DefaultStoppingLowestDoseHSRBeta .StoppingLowestDoseHSRBeta StoppingLowestDoseHSRBeta StoppingLowestDoseHSRBeta-class |
| 'StoppingMaxGainCIRatio' | .DefaultStoppingMaxGainCIRatio .StoppingMaxGainCIRatio StoppingMaxGainCIRatio StoppingMaxGainCIRatio-class |
| 'StoppingMinCohorts' | .DefaultStoppingMinCohorts .StoppingMinCohorts StoppingMinCohorts StoppingMinCohorts-class |
| 'StoppingMinPatients' | .DefaultStoppingMinPatients .StoppingMinPatients StoppingMinPatients StoppingMinPatients-class |
| 'StoppingMissingDose' | .DefaultStoppingMissingDose .StoppingMissingDose StoppingMissingDose StoppingMissingDose-class |
| 'StoppingMTDCV' | .DefaultStoppingMTDCV .StoppingMTDCV StoppingMTDCV StoppingMTDCV-class |
| 'StoppingMTDdistribution' | .DefaultStoppingMTDdistribution .StoppingMTDdistribution StoppingMTDdistribution StoppingMTDdistribution-class |
| 'StoppingOrdinal' | .DefaultStoppingOrdinal .StoppingOrdinal StoppingOrdinal StoppingOrdinal-class |
| 'StoppingPatientsNearDose' | .DefaultStoppingPatientsNearDose .StoppingPatientsNearDose StoppingPatientsNearDose StoppingPatientsNearDose-class |
| 'StoppingSpecificDose' | .DefaultStoppingSpecificDose .StoppingSpecificDose StoppingSpecificDose StoppingSpecificDose-class |
| 'StoppingTargetBiomarker' | .DefaultStoppingTargetBiomarker .StoppingTargetBiomarker StoppingTargetBiomarker StoppingTargetBiomarker-class |
| 'StoppingTargetProb' | .DefaultStoppingTargetProb .StoppingTargetProb StoppingTargetProb StoppingTargetProb-class |
| 'StoppingTDCIRatio' | .DefaultStoppingTDCIRatio .StoppingTDCIRatio 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,StoppingExternal,numeric,ANY,ANY,ANY-method stopTrial,StoppingHighestDose,numeric,ANY,ANY,Data-method stopTrial,StoppingList,ANY,ANY,ANY,ANY-method stopTrial,StoppingLowestDoseHSRBeta,numeric,Samples,ANY,ANY-method stopTrial,StoppingMaxGainCIRatio,ANY,missing,ModelTox,DataDual-method stopTrial,StoppingMaxGainCIRatio,ANY,Samples,ModelTox,DataDual-method stopTrial,StoppingMinCohorts,ANY,ANY,ANY,Data-method stopTrial,StoppingMinPatients,ANY,ANY,ANY,Data-method stopTrial,StoppingMissingDose,numeric,ANY,ANY,Data-method stopTrial,StoppingMTDCV,numeric,Samples,GeneralModel,ANY-method stopTrial,StoppingMTDdistribution,numeric,Samples,GeneralModel,ANY-method stopTrial,StoppingOrdinal,numeric,ANY,ANY,ANY-method stopTrial,StoppingOrdinal,numeric,ANY,LogisticLogNormalOrdinal,DataOrdinal-method stopTrial,StoppingPatientsNearDose,numeric,ANY,ANY,Data-method stopTrial,StoppingSpecificDose,numeric,ANY,ANY,Data-method stopTrial,StoppingTargetBiomarker,numeric,Samples,DualEndpoint,ANY-method stopTrial,StoppingTargetProb,numeric,Samples,GeneralModel,ANY-method stopTrial,StoppingTDCIRatio,ANY,missing,ModelTox,ANY-method stopTrial,StoppingTDCIRatio,ANY,Samples,ModelTox,ANY-method stopTrial-StoppingExternal stopTrial-StoppingLowestDoseHSRBeta stopTrial-StoppingMissingDose stopTrial-StoppingMTDCV stopTrial-StoppingOrdinal stopTrial-StoppingSpecificDose stopTrial-StoppingTargetBiomarker stopTrial-StoppingTargetProb |
| 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 |
| 'TDDesign' | .DefaultTDDesign .TDDesign TDDesign TDDesign-class |
| 'TDsamplesDesign' | .DefaultTDsamplesDesign .TDsamplesDesign TDsamplesDesign TDsamplesDesign-class |
| Tidying 'CrmPackClass' objects | tidy tidy,CohortSizeDLT-method tidy,CohortSizeMax-method tidy,CohortSizeMin-method tidy,CohortSizeParts-method tidy,CohortSizeRange-method tidy,CrmPackClass-method tidy,DataDA-method tidy,DataDual-method tidy,DataGrouped-method tidy,DataMixture-method tidy,DataOrdinal-method tidy,DataParts-method tidy,DualDesign-method tidy,Effloglog-method tidy,GeneralData-method tidy,IncrementsMaxToxProb-method tidy,IncrementsMin-method tidy,IncrementsRelative-method tidy,IncrementsRelativeDLT-method tidy,IncrementsRelativeParts-method tidy,LogisticIndepBeta-method tidy,NextBestNCRM-method tidy,NextBestNCRMLoss-method tidy,Samples-method tidy,Simulations-method tidy-CohortSizeDLT tidy-CohortSizeMax tidy-CohortSizeMin tidy-CohortSizeParts tidy-CohortSizeRange tidy-CrmPackClass tidy-DataDA tidy-DataDual tidy-DataGrouped tidy-DataMixture tidy-DataOrdinal tidy-DataParts tidy-DualDesign tidy-Effloglog tidy-GeneralData tidy-IncrementsMaxToxProb tidy-IncrementsMin tidy-IncrementsRelative tidy-IncrementsRelativeDLT tidy-IncrementsRelativeParts tidy-LogisticIndepBeta tidy-NextBestNCRM tidy-NextBestNCRMLoss tidy-Samples tidy-Simulations |
| 'TITELogisticLogNormal' | .DefaultTITELogisticLogNormal .TITELogisticLogNormal TITELogisticLogNormal TITELogisticLogNormal-class |
| Updating 'Data' Objects | update,Data-method update-Data |
| Updating 'DataDA' Objects | update,DataDA-method update-DataDA |
| Updating 'DataDual' Objects | update,DataDual-method update-DataDual |
| Updating 'DataOrdinal' Objects | update,DataOrdinal-method update-DataOrdinal |
| Updating 'DataParts' Objects | update,DataParts-method update-DataParts |
| Update method for the 'ModelPseudo' model class. This is a method to update the model class slots (estimates, parameters, variables and etc.), when the new data (e.g. new observations of responses) are available. This method is mostly used to obtain new modal estimates for pseudo model parameters. | update,ModelPseudo-method update-ModelPseudo |
| Internal Helper Functions for Validation of 'CohortSize' Objects | v_cohort_size v_cohort_size_const v_cohort_size_dlt v_cohort_size_max v_cohort_size_parts v_cohort_size_range |
| Internal Helper Functions for Validation of 'GeneralData' Objects | h_doses_unique_per_cohort v_data v_data_da v_data_dual v_data_grouped v_data_mixture v_data_objects v_data_ordinal v_data_parts v_general_data |
| Internal Helper Functions for Validation of 'RuleDesign' Objects | v_design v_design_grouped v_rule_design v_rule_design_ordinal |
| Internal Helper Functions for Validation of 'GeneralSimulations' Objects | v_da_simulations v_dual_simulations v_general_simulations v_simulations |
| Internal Helper Functions for Validation of 'Increments' Objects | v_cohort_size_ordinal v_increments v_increments_dose_levels v_increments_hsr_beta v_increments_maxtoxprob v_increments_min v_increments_ordinal v_increments_relative v_increments_relative_dlt v_increments_relative_parts |
| Internal Helper Functions for Validation of 'McmcOptions' Objects | v_mcmcoptions_objects v_mcmc_options |
| Internal Helper Functions for Validation of 'GeneralModel' and 'ModelPseudo' Objects | v_general_model v_logisticlognormalordinal v_model_da_logistic_log_normal v_model_dual_endpoint v_model_dual_endpoint_beta v_model_dual_endpoint_emax v_model_dual_endpoint_rw v_model_eff_flexi v_model_eff_log_log v_model_logistic_indep_beta v_model_logistic_kadane v_model_logistic_kadane_beta_gamma v_model_logistic_log_normal_mix v_model_logistic_normal_fixed_mix v_model_logistic_normal_mix v_model_objects v_model_one_par_exp_normal_prior v_model_one_par_exp_prior v_model_tite_logistic_log_normal |
| Internal Helper Functions for Validation of Model Parameters Objects | v_model_params v_model_params_normal |
| Internal Helper Functions for Validation of 'NextBest' Objects | v_next_best v_next_best_dual_endpoint v_next_best_inf_theory v_next_best_max_gain_samples v_next_best_min_dist v_next_best_mtd v_next_best_ncrm v_next_best_ncrm_loss v_next_best_ordinal v_next_best_prob_mtd_lte v_next_best_prob_mtd_min_dist v_next_best_td v_next_best_td_samples |
| Internal Helper Functions for Validation of 'PseudoSimulations' Objects | v_pseudo_dual_flex_simulations v_pseudo_dual_simulations v_pseudo_simulations |
| Internal Helper Functions for Validation of 'SafetyWindow' Objects | v_safety_window v_safety_window_const v_safety_window_size |
| Internal Helper Functions for Validation of 'Samples' Objects | v_samples v_samples_objects |
| Internal Helper Functions for Validation of 'Stopping' Objects | v_stopping v_stopping_all v_stopping_cohorts_near_dose v_stopping_list v_stopping_min_cohorts v_stopping_min_patients v_stopping_mtd_cv v_stopping_mtd_distribution v_stopping_patients_near_dose v_stopping_target_biomarker v_stopping_target_prob v_stopping_tdci_ratio |
| 'Validate' | Validate |
| Determine the safety window length of the next cohort | windowLength windowLength,SafetyWindowConst-method windowLength,SafetyWindowSize-method |