Package index

Package

crmPack-package crmPack

Object-oriented implementation of CRM designs

Classes

Validate stable

Validate

positive_number experimental

positive_number

CrmPackClass-class .CrmPackClass CrmPackClass experimental

CrmPackClass

.DefaultDataGeneral() stable

GeneralData

Data() .DefaultData() stable

Data

DataDual() .DefaultDataDual() stable

DataDual

DataOrdinal() .DefaultDataOrdinal() experimental

DataOrdinal

DataParts() .DefaultDataParts() stable

DataParts

DataMixture() .DefaultDataMixture() stable

DataMixture

DataDA() .DefaultDataDA() stable

DataDA

DataGrouped() .DefaultDataGrouped() stable

DataGrouped

McmcOptions() .DefaultMcmcOptions() stable

McmcOptions

ModelParamsNormal() .DefaultModelParamsNormal() experimental

ModelParamsNormal

.DefaultGeneralModel() stable

GeneralModel

ModelLogNormal() .DefaultModelLogNormal() stable

ModelLogNormal

LogisticNormal() .DefaultLogisticNormal() stable

LogisticNormal

LogisticLogNormal() .DefaultLogisticLogNormal() stable

LogisticLogNormal

LogisticLogNormalSub() .DefaultLogisticLogNormalSub() stable

LogisticLogNormalSub

ProbitLogNormal() .DefaultProbitLogNormal() stable

ProbitLogNormal

ProbitLogNormalRel() .DefaultProbitLogNormalRel() stable

ProbitLogNormalRel

LogisticLogNormalGrouped() .DefaultLogisticLogNormalGrouped() experimental

LogisticLogNormalGrouped

LogisticKadane() .DefaultLogisticKadane() stable

LogisticKadane

LogisticKadaneBetaGamma() .DefaultLogisticKadaneBetaGamma() experimental

LogisticKadaneBetaGamma

LogisticNormalMixture() .DefaultLogisticNormalMixture() stable

LogisticNormalMixture

LogisticNormalFixedMixture() .DefaultLogisticNormalFixedMixture() stable

LogisticNormalFixedMixture

LogisticLogNormalMixture() .DefaultLogisticLogNormalMixture() stable

LogisticLogNormalMixture

DualEndpoint() .DefaultDualEndpoint() experimental

DualEndpoint

DualEndpointRW() .DefaultDualEndpointRW() experimental

DualEndpointRW

DualEndpointBeta() .DefaultDualEndpointBeta() experimental

DualEndpointBeta

DualEndpointEmax() .DefaultDualEndpointEmax() experimental

DualEndpointEmax

.DefaultModelPseudo() stable

ModelPseudo

.DefaultModelTox() stable

ModelTox

.DefaultModelEff() stable

ModelEff

LogisticIndepBeta() .DefaultLogisticIndepBeta() stable

LogisticIndepBeta

Effloglog() .DefaultEffloglog() stable

Effloglog

EffFlexi() .DefaultEffFlexi() stable

EffFlexi

DALogisticLogNormal() .DefaultDALogisticLogNormal() stable

DALogisticLogNormal

TITELogisticLogNormal() .DefaultTITELogisticLogNormal() stable

TITELogisticLogNormal

OneParLogNormalPrior() .DefaultOneParLogNormalPrior() stable

OneParLogNormalPrior

OneParExpPrior() .DefaultOneParExpPrior() experimental

OneParExpPrior

FractionalCRM() .DefaultFractionalCRM() stable

FractionalCRM

Samples() .DefaultSamples() stable

Samples

.DefaultNextBest() stable

NextBest

NextBestEWOC() .DefaultNextBestEWOC() stable

NextBestEWOC

NextBestMTD() .DefaultNextBestMTD() stable

NextBestMTD

NextBestNCRM() .DefaultNextBestNCRM() stable

NextBestNCRM

NextBestNCRMLoss() .DefaultNextBestNCRMLoss() stable

NextBestNCRMLoss

NextBestThreePlusThree() .DefaultNextBestThreePlusThree() stable

NextBestThreePlusThree

NextBestDualEndpoint() .DefaultNextBestDualEndpoint() experimental

NextBestDualEndpoint

NextBestMinDist() .DefaultNextBestMinDist() stable

NextBestMinDist

NextBestInfTheory() .DefaultNextBestInfTheory() stable

NextBestInfTheory

.DefaultNextBestTD() NextBestTD() stable

NextBestTD

NextBestTDsamples() .DefaultNextBestTDsamples() stable

NextBestTDsamples

NextBestMaxGain() .DefaultNextBestMaxGain() stable

NextBestMaxGain

NextBestMaxGainSamples() .DefaultNextBestMaxGainSamples() stable

NextBestMaxGainSamples

NextBestProbMTDLTE() .DefaultNextBestProbMTDLTE() experimental

NextBestProbMTDLTE

NextBestProbMTDMinDist() .DefaultNextBestProbMTDMinDist() experimental

NextBestProbMTDMinDist

.DefaultIncrements() stable

Increments

IncrementsRelative() .DefaultIncrementsRelative() stable

IncrementsRelative

IncrementsRelativeParts() .DefaultIncrementsRelativeParts() stable

IncrementsRelativeParts

IncrementsRelativeDLT() .DefaultIncrementsRelativeDLT() stable

IncrementsRelativeDLT

IncrementsRelativeDLTCurrent() .DefaultIncrementsRelativeDLTCurrent() experimental

IncrementsRelativeDLTCurrent

IncrementsDoseLevels() .DefaultIncrementsDoseLevels() stable

IncrementsDoseLevels

IncrementsHSRBeta() .DefaultIncrementsHSRBeta() experimental

IncrementsHSRBeta

IncrementsMin() .DefaultIncrementsMin() stable

IncrementsMin

IncrementsMaxToxProb() .DefaultIncrementsMaxToxProb() experimental

IncrementsMaxToxProb

Stopping-class Stopping stable

Stopping

StoppingMissingDose() .DefaultStoppingMissingDose() experimental

StoppingMissingDose

StoppingCohortsNearDose() .DefaultStoppingCohortsNearDose() stable

StoppingCohortsNearDose

StoppingPatientsNearDose() .DefaultStoppingPatientsNearDose() stable

StoppingPatientsNearDose

StoppingMinCohorts() .DefaultStoppingMinCohorts() stable

StoppingMinCohorts

StoppingMinPatients() .DefaultStoppingMinPatients() stable

StoppingMinPatients

StoppingTargetProb() .DefaultStoppingTargetProb() stable

StoppingTargetProb

StoppingMTDdistribution() .DefaultStoppingMTDdistribution() stable

StoppingMTDdistribution

StoppingMTDCV() .DefaultStoppingMTDCV() experimental

StoppingMTDCV

StoppingLowestDoseHSRBeta() .DefaultStoppingLowestDoseHSRBeta() experimental

StoppingLowestDoseHSRBeta

StoppingTargetBiomarker() .DefaultStoppingTargetBiomarker() stable

StoppingTargetBiomarker

StoppingSpecificDose() .DefaultStoppingSpecificDose() experimental

StoppingSpecificDose

StoppingHighestDose() .DefaultStoppingHighestDose() experimental

StoppingHighestDose

StoppingList() .DefaultStoppingList() stable

StoppingList

StoppingAll() .DefaultStoppingAll() stable

StoppingAll

StoppingAny() .DefaultStoppingAny() stable

StoppingAny

StoppingTDCIRatio() .DefaultStoppingTDCIRatio() stable

StoppingTDCIRatio

StoppingMaxGainCIRatio() .DefaultStoppingMaxGainCIRatio() stable

StoppingMaxGainCIRatio

StoppingExternal() .DefaultStoppingExternal() experimental

StoppingExternal

.DefaultCohortSize() stable

CohortSize

CohortSizeRange() .DefaultCohortSizeRange() stable

CohortSizeRange

CohortSizeDLT() .DefaultCohortSizeDLT() stable

CohortSizeDLT

CohortSizeConst() .DefaultCohortSizeConst() stable

CohortSizeConst

CohortSizeParts() .DefaultCohortSizeParts() stable

CohortSizeParts

.DefaultCohortSizeMax() CohortSizeMax() stable

CohortSizeMax

CohortSizeMin() .DefaultCohortSizeMin() stable

CohortSizeMin

.DefaultSafetyWindow() stable

SafetyWindow

SafetyWindowSize() .DefaultSafetyWindowSize() stable

SafetyWindowSize

SafetyWindowConst() .DefaultSafetyWindowConst() stable

SafetyWindowConst

RuleDesign() .DefaultRuleDesign() ThreePlusThreeDesign() stable

RuleDesign

Design() .DefaultDesign() stable

Design

DesignOrdinal() .DefaultDesignOrdinal() experimental

DesignOrdinal

RuleDesignOrdinal() .DefaultRuleDesignOrdinal() experimental

RuleDesignOrdinal

DualDesign() .DefaultDualDesign() stable

DualDesign

TDsamplesDesign() .DefaultTDsamplesDesign() stable

TDsamplesDesign

TDDesign() .DefaultTDDesign() stable

TDDesign

DesignGrouped() experimental

DesignGrouped

Internal Helper Functions

h_blind_plot_data()

Helper Function to Blind Plot Data

h_convert_ordinal_data() experimental

Convert a Ordinal Data to the Equivalent Binary Data for a Specific Grade

h_convert_ordinal_model() experimental

Convert an ordinal CRM model to the Equivalent Binary CRM Model for a Specific Grade

h_convert_ordinal_samples() experimental

Convert a Samples Object from an ordinal Model to the Equivalent Samples Object from a Binary Model

v_general_data() h_doses_unique_per_cohort() v_data() v_data_dual() v_data_parts() v_data_mixture() v_data_da() v_data_ordinal() v_data_grouped() stable

Internal Helper Functions for Validation of GeneralData Objects

h_all_equivalent() experimental

Comparison with Numerical Tolerance and Without Name Comparison

h_plot_data_df() experimental

Preparing Data for Plotting

h_plot_data_cohort_lines() experimental

Preparing Cohort Lines for Data Plot

h_check_fun_formals() experimental

Checking Formals of a Function

h_slots() experimental

Getting the Slots from a S4 Object

h_format_number() experimental

Conditional Formatting Using C-style Formats

h_rapply() experimental

Recursively Apply a Function to a List

h_null_if_na() stable

Getting NULL for NA

h_is_positive_definite() experimental

Testing Matrix for Positive Definiteness

h_test_named_numeric() stable

Check that an argument is a named vector of type numeric

h_in_range() stable

Check which elements are in a given range

h_find_interval() stable

Find Interval Numbers or Indices and Return Custom Number For 0.

h_validate_combine_results() experimental

Combining S4 Class Validation Results

h_jags_add_dummy() experimental

Appending a Dummy Number for Selected Slots in Data

h_jags_join_models() stable

Joining JAGS Models

h_jags_get_model_inits() experimental

Setting Initial Values for JAGS Model Parameters

h_jags_get_data() experimental

Getting Data for JAGS

h_jags_write_model() stable

Writing JAGS Model to a File

h_jags_extract_samples() stable

Extracting Samples from JAGS mcarray Object

h_model_dual_endpoint_sigma2w() stable

Update DualEndpoint class model components with regard to biomarker regression variance.

h_model_dual_endpoint_rho() stable

Update DualEndpoint class model components with regard to DLT and biomarker correlation.

h_model_dual_endpoint_sigma2betaw() stable

Update certain components of DualEndpoint model with regard to prior variance factor of the random walk.

h_model_dual_endpoint_beta() stable

Update certain components of DualEndpoint model with regard to parameters of the function that models dose-biomarker relationship defined in the DualEndpointBeta class.

h_info_theory_dist() experimental

Calculating the Information Theoretic Distance

h_next_best_mg_ci() experimental

Credibility Intervals for Max Gain and Target Doses at nextBest-NextBestMaxGain Method.

h_next_best_mg_doses_at_grid() experimental

Get Closest Grid Doses for a Given Target Doses for nextBest-NextBestMaxGain Method.

h_next_best_eligible_doses() experimental

Get Eligible Doses from the Dose Grid.

h_next_best_ncrm_loss_plot() experimental

Building the Plot for nextBest-NextBestNCRMLoss Method.

h_next_best_tdsamples_plot() experimental

Building the Plot for nextBest-NextBestTDsamples Method.

h_next_best_td_plot() experimental

Building the Plot for nextBest-NextBestTD Method.

h_next_best_mg_plot() experimental

Building the Plot for nextBest-NextBestMaxGain Method.

h_next_best_mgsamples_plot() experimental

Building the Plot for nextBest-NextBestMaxGainSamples Method.

h_obtain_dose_grid_range()

Helper Function Containing Common Functionality

h_covr_active() h_covr_detrace() h_is_covr_trace() h_covr_detrace_call()

Helpers for stripping expressions of covr-inserted trace code

h_default_if_empty() stable

Getting the default value for an empty object

h_unpack_stopit()

Helper function to recursively unpack stopping rules and return lists with logical value and label given

h_calc_report_label_percentage()

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_validate_common_data_slots()

Helper Function performing validation Common to Data and DataOrdinal

h_summarize_add_stats()

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_determine_dlts()

Helper function to determine the dlts including first separate and placebo condition

Internal Validation Functions

v_general_data() h_doses_unique_per_cohort() v_data() v_data_dual() v_data_parts() v_data_mixture() v_data_da() v_data_ordinal() v_data_grouped() stable

Internal Helper Functions for Validation of GeneralData Objects

v_mcmc_options() stable

Internal Helper Functions for Validation of McmcOptions Objects

v_model_params_normal() experimental

Internal Helper Functions for Validation of Model Parameters Objects

v_general_model() v_model_logistic_kadane() v_model_logistic_kadane_beta_gamma() v_model_logistic_normal_mix() v_model_logistic_normal_fixed_mix() v_model_logistic_log_normal_mix() v_model_dual_endpoint() v_model_dual_endpoint_rw() v_model_dual_endpoint_beta() v_model_dual_endpoint_emax() v_model_logistic_indep_beta() v_model_eff_log_log() v_model_eff_flexi() v_model_da_logistic_log_normal() v_model_tite_logistic_log_normal() v_model_one_par_exp_normal_prior() v_model_one_par_exp_prior() v_logisticlognormalordinal() stable

Internal Helper Functions for Validation of GeneralModel and ModelPseudo Objects

v_samples()

Internal Helper Functions for Validation of Samples Objects

v_next_best_mtd() v_next_best_ncrm() v_next_best_ncrm_loss() v_next_best_dual_endpoint() v_next_best_min_dist() v_next_best_ewoc() v_next_best_inf_theory() v_next_best_td() v_next_best_td_samples() v_next_best_max_gain_samples() v_next_best_prob_mtd_lte() v_next_best_prob_mtd_min_dist() v_next_best_ordinal() stable

Internal Helper Functions for Validation of NextBest Objects

v_increments_relative() v_increments_relative_parts() v_increments_relative_dlt() v_increments_dose_levels() v_increments_hsr_beta() v_increments_min() v_increments_maxtoxprob() v_increments_ordinal() v_cohort_size_ordinal() stable

Internal Helper Functions for Validation of Increments Objects

v_starting_dose() experimental

Internal Helper Functions for Validation of StartingDose Objects

v_stopping_cohorts_near_dose() v_stopping_patients_near_dose() v_stopping_min_cohorts() v_stopping_min_patients() v_stopping_target_prob() v_stopping_mtd_distribution() v_stopping_mtd_cv() v_stopping_target_biomarker() v_stopping_list() v_stopping_all() v_stopping_tdci_ratio() stable

Internal Helper Functions for Validation of Stopping Objects

v_cohort_size_range() v_cohort_size_dlt() v_cohort_size_const() v_cohort_size_parts() v_cohort_size_max() stable

Internal Helper Functions for Validation of CohortSize Objects

v_safety_window_size() v_safety_window_const() stable

Internal Helper Functions for Validation of SafetyWindow Objects

v_rule_design() v_rule_design_ordinal() v_design_grouped() stable

Internal Helper Functions for Validation of RuleDesign Objects

v_general_simulations() v_simulations() v_dual_simulations() v_da_simulations() stable

Internal Helper Functions for Validation of GeneralSimulations Objects

v_pseudo_simulations() v_pseudo_dual_simulations() v_pseudo_dual_flex_simulations() stable

Internal Helper Functions for Validation of PseudoSimulations Objects

Custom Checkmate Assertions

check_probabilities() assert_probabilities() test_probabilities() expect_probabilities() stable

Check if an argument is a probability vector

check_probability() assert_probability() test_probability() expect_probability() stable

Check if an argument is a single probability value

check_probability_range() assert_probability_range() test_probability_range() expect_probability_range() stable

Check if an argument is a probability range

check_length() assert_length() test_length() stable

Check if vectors are of compatible lengths

check_range() assert_range() test_range() expect_range() stable

Check that an argument is a numerical range

Methods

h_plot_data_dataordinal() plot(<Data>,<missing>) plot(<DataOrdinal>,<missing>) stable

Helper Function for the Plot Method of the Data and DataOrdinal Classes

plot(<DataDual>,<missing>) stable

Plot Method for the DataDual Class

plot(<DataDA>,<missing>) stable

Plot Method for the DataDA Class

update(<Data>) stable

Updating Data Objects

update(<DataParts>) stable

Updating DataParts Objects

update(<DataDual>) stable

Updating DataDual Objects

update(<DataDA>) stable

Updating DataDA Objects

update(<DataOrdinal>) experimental

Updating DataOrdinal Objects

getEff() stable

Extracting Efficacy Responses for Subjects Categorized by the DLT

ngrid() stable

Number of Doses in Grid

dose_grid_range() stable

Getting the Dose Grid Range

saveSample() stable

Determining if this Sample Should be Saved

size() stable

Size of an Object

doseFunction() experimental

Getting the Dose Function for a Given Model Type

dose() stable

Computing the Doses for a given independent variable, Model and Samples

probFunction() experimental

Getting the Prob Function for a Given Model Type

prob() stable

Computing Toxicity Probabilities for a Given Dose, Model and Samples

efficacyFunction() experimental

Getting the Efficacy Function for a Given Model Type

efficacy() stable

Computing Expected Efficacy for a Given Dose, Model and Samples

biomarker() experimental

Get the Biomarker Levels for a Given Dual-Endpoint Model, Given Dose Levels and Samples

gain() stable

Compute Gain Values based on Pseudo DLE and a Pseudo Efficacy Models and Using Optional Samples.

update(<ModelPseudo>)

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.

mcmc() stable

Obtaining Posterior Samples for all Model Parameters

names(<Samples>) stable

The Names of the Sampled Parameters

nextBest() stable

Finding the Next Best Dose

stopTrial() stable

Stop the trial?

maxDose() stable

Determine the Maximum Possible Next Dose

Functions

enable_logging() disable_logging() is_logging_enabled() log_trace() experimental

Verbose Logging

dapply() experimental

Apply a Function to Subsets of Data Frame.

knit_print(<CohortSizeConst>) knit_print(<CohortSizeRange>) knit_print(<CohortSizeDLT>) knit_print(<CohortSizeParts>) knit_print(<CohortSizeMax>) knit_print(<CohortSizeMin>) knit_print(<CohortSizeOrdinal>) knit_print(<StartingDose>) knit_print(<RuleDesign>) knit_print(<Design>) knit_print(<DualDesign>) knit_print(<DADesign>) knit_print(<TDDesign>) knit_print(<DualResponsesDesign>) knit_print(<DesignOrdinal>) knit_print(<DesignGrouped>) knit_print(<TDsamplesDesign>) knit_print(<DualResponsesSamplesDesign>) knit_print(<RuleDesignOrdinal>) knit_print(<GeneralData>) knit_print(<DataParts>) knit_print(<DualEndpoint>) knit_print(<ModelParamsNormal>) knit_print(<GeneralModel>) knit_print(<LogisticKadane>) knit_print(<LogisticKadaneBetaGamma>) knit_print(<LogisticLogNormal>) knit_print(<LogisticLogNormalMixture>) knit_print(<LogisticLogNormalSub>) knit_print(<LogisticNormalMixture>) knit_print(<LogisticNormalFixedMixture>) knit_print(<OneParLogNormalPrior>) knit_print(<OneParExpPrior>) knit_print(<LogisticLogNormalGrouped>) knit_print(<LogisticLogNormalOrdinal>) knit_print(<LogisticIndepBeta>) knit_print(<Effloglog>) knit_print(<IncrementsRelative>) knit_print(<IncrementsRelativeDLT>) knit_print(<IncrementsDoseLevels>) knit_print(<IncrementsHSRBeta>) knit_print(<IncrementsMin>) knit_print(<IncrementsOrdinal>) knit_print(<IncrementsRelativeParts>) knit_print(<IncrementsRelativeDLTCurrent>) knit_print(<NextBestMTD>) knit_print(<NextBestNCRM>) knit_print(<NextBestThreePlusThree>) knit_print(<NextBestDualEndpoint>) knit_print(<NextBestMinDist>) knit_print(<NextBestInfTheory>) knit_print(<NextBestTD>) knit_print(<NextBestMaxGain>) knit_print(<NextBestProbMTDLTE>) knit_print(<NextBestProbMTDMinDist>) knit_print(<NextBestNCRMLoss>) knit_print(<NextBestTDsamples>) knit_print(<NextBestMaxGainSamples>) knit_print(<NextBestOrdinal>) knit_print(<SafetyWindow>) knit_print(<SafetyWindowConst>) knit_print(<SafetyWindowSize>) knit_print(<StoppingOrdinal>) knit_print(<StoppingMaxGainCIRatio>) knit_print(<StoppingList>) knit_print(<StoppingAny>) knit_print(<StoppingAll>) knit_print(<StoppingTDCIRatio>) knit_print(<StoppingTargetBiomarker>) knit_print(<StoppingLowestDoseHSRBeta>) knit_print(<StoppingMTDCV>) knit_print(<StoppingMTDdistribution>) knit_print(<StoppingHighestDose>) knit_print(<StoppingSpecificDose>) knit_print(<StoppingTargetProb>) knit_print(<StoppingMinCohorts>) knit_print(<StoppingMinPatients>) knit_print(<StoppingPatientsNearDose>) knit_print(<StoppingCohortsNearDose>) knit_print(<StoppingMissingDose>) experimental

Render a CohortSizeConst Object

Classes

CohortSizeOrdinal() .DefaultCohortSizeOrdinal() experimental

CohortSizeOrdinal

IncrementsOrdinal() .DefaultIncrementsOrdinal() experimental

IncrementsOrdinal

DADesign() .DefaultDADesign() stable

DADesign

DASimulations() .DefaultDASimulations() stable

DASimulations

DualResponsesDesign() .DefaultDualResponsesDesign() stable

DualResponsesDesign.R

DualResponsesSamplesDesign() .DefaultDualResponsesSamplesDesign() stable

DualResponsesSamplesDesign

DualSimulations() .DefaultDualSimulations() stable

DualSimulations

.DefaultDualSimulationsSummary() stable

DualSimulationsSummary

GeneralSimulations() .DefaultGeneralSimulations() stable

GeneralSimulations

.DefaultGeneralSimulationsSummary() stable

GeneralSimulationsSummary

IncrementsMaxToxProb() .DefaultIncrementsMaxToxProb() experimental

IncrementsMaxToxProb

LogisticLogNormalOrdinal() .DefaultLogisticLogNormalOrdinal() experimental

LogisticLogNormalOrdinal

MinimalInformative() stable

Construct a Minimally Informative Prior

NextBestOrdinal() .DefaultNextBestOrdinal() experimental

NextBestOrdinal

PseudoDualFlexiSimulations() .DefaultPseudoDualFlexiSimulations() stable

PseudoDualFlexiSimulations

PseudoDualSimulations() .DefaultPseudoDualSimulations() stable

PseudoDualSimulations

.DefaultPseudoDualSimulationsSummary() stable

PseudoDualSimulationsSummary

PseudoSimulations() .DefaultPseudoSimulations() stable

PseudoSimulations

.DefaultPseudoSimulationsSummary() stable

PseudoSimulationsSummary

Quantiles2LogisticNormal() stable

Convert Prior Quantiles to Logistic (Log) Normal Model

Simulations() .DefaultSimulations() stable

Simulations

.DefaultSimulationsSummary() stable

SimulationsSummary

StoppingOrdinal() .DefaultStoppingOrdinal() experimental

StoppingOrdinal

approximate()

Approximate posterior with (log) normal distribution

assertions experimental

Additional Assertions for checkmate

check_equal() assert_equal() experimental

Check if All Arguments Are Equal

check_format() assert_format() test_format() expect_format() stable

Check that an argument is a valid format specification

crmPackExample() stable

Open the Example PDF for crmPack

crmPackHelp() stable

Open the Browser with Help Pages for crmPack

examine()

Obtain Hypothetical Trial Course Table for a Design

fit()

Fit method for the Samples class

fitGain()

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

fitPEM()

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,

get(<Samples>,<character>)

Get specific parameter samples and produce a data.frame

h_get_min_inf_beta()

Helper for Minimal Informative Unimodal Beta Distribution

logit() stable

Shorthand for Logit Function

match_within_tolerance() stable

Helper Function for Value Matching with Tolerance

maxSize() stable

"MAX" Combination of Cohort Size Rules

minSize() stable

"MIN" Combination of Cohort Size Rules

`|`(<Stopping>,<Stopping>) stable

Combine Two Stopping Rules with OR

`|`(<Stopping>,<StoppingAny>) stable

Combine an Atomic Stopping Rule and a Stopping List with OR

`|`(<StoppingAny>,<Stopping>) stable

Combine a Stopping List and an Atomic Stopping Rule with OR

plot(<Data>,<ModelTox>)

Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples

plot(<DataDual>,<ModelEff>)

Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples

plot(<DualSimulations>,<missing>) stable

Plot DualSimulations

plot(<DualSimulationsSummary>,<missing>) stable

Plot Dual-Endpoint Design Simulation Summary

plot(<GeneralSimulations>,<missing>) stable

Plot GeneralSimulations

plot(<GeneralSimulationsSummary>,<missing>) stable

Plot GeneralSimulationsSummary

plot(<PseudoDualFlexiSimulations>,<missing>) stable

Plot PseudoDualFlexiSimulations

plot(<PseudoDualSimulations>,<missing>) stable

Plot PseudoDualSimulations

plot(<PseudoDualSimulationsSummary>,<missing>) stable

Plot PseudoDualSimulationsSummary

plot(<PseudoSimulationsSummary>,<missing>) stable

Plot PseudoSimulationsSummary

plot(<Samples>,<DALogisticLogNormal>)

Plotting dose-toxicity model fits

plot(<Samples>,<DualEndpoint>)

Plotting dose-toxicity and dose-biomarker model fits

plot(<Samples>,<GeneralModel>)

Plotting dose-toxicity model fits

plot(<Samples>,<ModelEff>)

Plot the fitted dose-efficacy curve using a model from ModelEff class with samples

plot(<Samples>,<ModelTox>)

Plot the fitted dose-DLE curve using a ModelTox class model with samples

plot(<SimulationsSummary>,<missing>) stable

Plot Model-Based Design Simulation Summary

plotDualResponses()

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

plotGain()

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

plot(<gtable>) print(<gtable>) stable

Plot gtable Objects

probit() stable

Shorthand for Probit Function

set_seed() stable

Helper Function to Set and Save the RNG Seed

show(<DualSimulationsSummary>) stable

Show the Summary of Dual-Endpoint Simulations

show(<GeneralSimulationsSummary>) stable

Show the Summary of the Simulations

show(<PseudoDualSimulationsSummary>) stable

Show the Summary of PseudoDualSimulations

show(<PseudoSimulationsSummary>) stable

Show the Summary of PseudoSimulations

show(<SimulationsSummary>) stable

Show the Summary of Model-Based Design Simulations

simulate(<DADesign>) stable

Simulate outcomes from a time-to-DLT augmented CRM design

simulate(<Design>) stable

Simulate outcomes from a CRM design

simulate(<DualDesign>) stable

Simulate outcomes from a dual-endpoint design

simulate(<DualResponsesDesign>) stable

Simulate dose escalation procedure using both DLE and efficacy responses without samples

simulate(<DualResponsesSamplesDesign>) stable

Simulate dose escalation procedure using DLE and efficacy responses with samples

simulate(<RuleDesign>) stable

Simulate outcomes from a rule-based design

simulate(<TDDesign>) stable

Simulate dose escalation procedure using DLE responses only without samples

simulate(<TDsamplesDesign>) stable

Simulate dose escalation procedure using DLE responses only with DLE samples

simulate(<DesignGrouped>) experimental

Simulate Method for the DesignGrouped Class

summary(<DualSimulations>) stable

Summarize Dual-Endpoint Design Simulations

summary(<GeneralSimulations>) stable

Summarize the GeneralSimulations, Relative to a Given Truth

summary(<PseudoDualFlexiSimulations>) stable

Summarize PseudoDualFlexiSimulations

summary(<PseudoDualSimulations>) stable

Summarize PseudoDualSimulations

summary(<Simulations>) stable

Summarize Model-Based Design Simulations

summary(<PseudoSimulations>) stable

Summarize PseudoSimulations

tidy() experimental

Tidying CrmPackClass objects

windowLength() stable

Determine the Safety Window Length of the Next Cohort

`&`(<Stopping>,<Stopping>) stable

Combine Two Stopping Rules with AND

`&`(<Stopping>,<StoppingAll>) stable

Combine an Atomic Stopping Rule and a Stopping List with AND

`&`(<StoppingAll>,<Stopping>) stable

Combine a Stopping List and an Atomic Stopping Rule with AND