Object-Oriented Implementation of CRM Designs

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Documentation for package ‘crmPack’ version 1.0.6

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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A B C D E F G I L M N O P Q R S T U V W misc

crmPack-package

Object-oriented implementation of CRM designs

-- A --

AllModels-class	Class for All models This is a class where all models inherit.
approximate	Approximate posterior with (log) normal distribution
approximate-method	Approximate posterior with (log) normal distribution
as.list-method	as.list method for the "GeneralData" class

-- B --

biomLevel	Compute the biomarker level for a given dose, given model and samples
biomLevel-method	Compute the biomarker level for a given dose, given model and samples

-- C --

CohortSize-class	The virtual class for cohort sizes
CohortSizeConst	Initialization function for "CohortSizeConst"
CohortSizeConst-class	Constant cohort size
CohortSizeDLT	Initialization function for "CohortSizeDLT"
CohortSizeDLT-class	Cohort size based on number of DLTs
CohortSizeMax	Initialization function for "CohortSizeMax"
CohortSizeMax-class	Size based on maximum of multiple cohort size rules
CohortSizeMin	Initialization function for "CohortSizeMin"
CohortSizeMin-class	Size based on minimum of multiple cohort size rules
CohortSizeParts	Initialization function for "CohortSizeParts"
CohortSizeParts-class	Cohort size based on the parts
CohortSizeRange	Initialization function for "CohortSizeRange"
CohortSizeRange-class	Cohort size based on dose range
crmPack	Object-oriented implementation of CRM designs
crmPackExample	Open the example pdf for crmPack
crmPackHelp	Open the browser with help pages for crmPack

-- D --

Data	Initialization function for the "Data" class
Data-class	Class for the data input
DataDual	Initialization function for the "DataDual" class
DataDual-class	Class for the dual endpoint data input
DataMixture	Initialization function for the "DataMixture" class
DataMixture-class	Class for the data with mixture sharing
DataParts	Initialization function for the "DataParts" class
DataParts-class	Class for the data with two study parts
Design	Initialization function for "Design"
Design-class	Class for the CRM design
dose	Compute the doses for a given probability, given model and samples
dose-method	Compute the doses for a given probability, given model and samples
DualDesign	Initialization function for "DualDesign"
DualDesign-class	Class for the dual-endpoint CRM design
DualEndpoint	Initialization function for the "DualEndpoint" class
DualEndpoint-class	General class for the dual endpoint model
DualEndpointBeta	Initialization function for the "DualEndpointBeta" class
DualEndpointBeta-class	Dual endpoint model with beta function for dose-biomarker relationship
DualEndpointEmax	Initialization function for the "DualEndpointEmax" class
DualEndpointEmax-class	Dual endpoint model with emax function for dose-biomarker relationship
DualEndpointRW	Initialization function for the "DualEndpointRW" class
DualEndpointRW-class	Dual endpoint model with RW prior for biomarker
DualResponsesDesign	Initialization function for 'DualResponsesDesign"
DualResponsesDesign-class	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
DualResponsesSamplesDesign	Initialization function for 'DualResponsesSamplesDesign"
DualResponsesSamplesDesign-class	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
DualSimulations	Initialization function for "DualSimulations"
DualSimulations-class	Class for the simulations output from dual-endpoint model based designs
DualSimulationsSummary-class	Class for the summary of dual-endpoint simulations output

-- E --

EffFlexi	Initialization function for the "EffFlexi" class
EffFlexi-class	Class for the efficacy model in flexible form for prior expressed in form of pseudo data
Effloglog	Initialization function for the "Effloglog" class
Effloglog-class	Class for the linear log-log efficacy model using pseudo data prior
examine	Obtain hypothetical trial course table for a design
examine-method	Obtain hypothetical trial course table for a design
ExpEff	Compute the expected efficacy based on a given dose, a given pseudo Efficacy log-log model and a given efficacy sample
ExpEff-method	Compute the expected efficacy based on a given dose, a given pseudo Efficacy log-log model and a given efficacy sample

-- F --

fit	Fit method for the Samples class
fit-method	Fit method for the Samples class
fitGain	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-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

-- G --

gain	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-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
GeneralData-class	Class for general data input
GeneralModel-class	No Intitialization function for this General class for model input
GeneralSimulations	Initialization function for "GeneralSimulations"
GeneralSimulations-class	General class for the simulations output
GeneralSimulationsSummary-class	Class for the summary of general simulations output
get-method	Get specific parameter samples and produce a data.frame
getEff	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-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)
getMinInfBeta	Get the minimal informative unimodal beta distribution

-- I --

IncrementMin	Initialization function for "IncrementMin"
IncrementMin-class	Max increment based on minimum of multiple increment rules
Increments-class	The virtual class for controlling increments
IncrementsNumDoseLevels	Initialization function for "IncrementsNumDoseLevels"
IncrementsNumDoseLevels-class	Increments control based on number of dose levels
IncrementsRelative	Initialization function for "IncrementsRelative"
IncrementsRelative-class	Increments control based on relative differences in intervals
IncrementsRelativeDLT	Initialization function for "IncrementsRelativeDLT"
IncrementsRelativeDLT-class	Increments control based on relative differences in terms of DLTs
IncrementsRelativeParts	Initialization function for "IncrementsRelativeParts"
IncrementsRelativeParts-class	Increments control based on relative differences in intervals, with special rules for part 1 and beginning of part 2
initialize-method	Initialization method for the "DualEndpointOld" class

-- L --

LogisticIndepBeta	Intialization function for "LogisticIndepBeta" class
LogisticIndepBeta-class	No initialization function Standard logistic model with prior in form of pseudo data
LogisticKadane	Initialization function for the "LogisticKadane" class
LogisticKadane-class	Reparametrized logistic model
LogisticLogNormal	Initialization function for the "LogisticLogNormal" class
LogisticLogNormal-class	Standard logistic model with bivariate (log) normal prior
LogisticLogNormalMixture	Initialization function for the "LogisticLogNormalMixture" class
LogisticLogNormalMixture-class	Standard logistic model with online mixture of two bivariate log normal priors
LogisticLogNormalSub	Initialization function for the "LogisticLogNormalSub" class
LogisticLogNormalSub-class	Standard logistic model with bivariate (log) normal prior with substractive dose standardization
LogisticNormal	Initialization function for the "LogisticNormal" class
LogisticNormal-class	Standard logistic model with bivariate normal prior
LogisticNormalFixedMixture	Initialization function for the "LogisticNormalFixedMixture" class
LogisticNormalFixedMixture-class	Standard logistic model with fixed mixture of multiple bivariate (log) normal priors
LogisticNormalMixture	Initialization function for the "LogisticNormalMixture" class
LogisticNormalMixture-class	Standard logistic model with flexible mixture of two bivariate normal priors
logit	Shorthand for logit function

-- M --

matchTolerance	Helper function for value matching with tolerance
maxDose	Determine the maximum possible next dose
maxDose-method	Determine the maximum possible next dose
maxSize	"MAX" combination of cohort size rules
maxSize-method	"MAX" combination of cohort size rules
mcmc	Obtain posterior samples for all model parameters
mcmc-method	Obtain posterior samples for all model parameters
McmcOptions	Initialization function for the "McmcOptions" class
McmcOptions-class	Class for the three canonical MCMC options
MinimalInformative	Construct a minimally informative prior
minSize	"MIN" combination of cohort size rules
minSize-method	"MIN" combination of cohort size rules
Model-class	Class for the model input
ModelEff-class	No Initialization function class for Efficacy models using pseudo data prior
ModelPseudo-class	Class of models using expressing their prior in form of Pseudo data
ModelTox-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'
multiplot	Multiple plot function

-- N --

nextBest	Find the next best dose
NextBest-class	The virtual class for finding next best dose
nextBest-method	Find the next best dose
NextBestDualEndpoint	Initialization function for "NextBestDualEndpoint"
NextBestDualEndpoint-class	The class with the input for finding the next dose based on the dual endpoint model
NextBestMaxGain	Initialization function for the class 'NextBestMaxGain'
NextBestMaxGain-class	Next best dose with maximum gain value based on a pseudo DLE and efficacy model without samples
NextBestMaxGainSamples	Initialization function for class "NextBestMaxGainSamples"
NextBestMaxGainSamples-class	Next best dose with maximum gain value based on a pseudo DLE and efficacy model with samples
NextBestMTD	Initialization function for class "NextBestMTD"
NextBestMTD-class	The class with the input for finding the next best MTD estimate
NextBestNCRM	Initialization function for "NextBestNCRM"
NextBestNCRM-class	The class with the input for finding the next dose in target interval
NextBestTD	Initialization function for the class "NextBestTD"
NextBestTD-class	Next best dose based on Pseudo DLE model without sample
NextBestTDsamples	Initialization function for class "NextBestTDsamples"
NextBestTDsamples-class	Next best dose based on Pseudo DLE Model with samples
NextBestThreePlusThree	Initialization function for "NextBestThreePlusThree"
NextBestThreePlusThree-class	The class with the input for finding the next dose in target interval

-- O --

or-Stopping-Stopping	The method combining two atomic stopping rules
or-Stopping-StoppingAny	The method combining a stopping list and an atomic
or-StoppingAny-Stopping	The method combining an atomic and a stopping list

-- P --

plot-method	Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples
plot-method	Plot method for the "Data" class
plot-method	Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples
plot-method	Plot method for the "DataDual" class
plot-method	Plot dual-endpoint simulations
plot-method	Plot summaries of the dual-endpoint design simulations
plot-method	Plot simulations
plot-method	Graphical display of the general simulation summary
plot-method	Plot for PseudoDualFlexiSimulations
plot-method	Plot simulations
plot-method	Plot the summary of Pseudo Dual Simulations summary
plot-method	Plot summaries of the pseudo simulations
plot-method	Plotting dose-toxicity and dose-biomarker model fits
plot-method	Plotting dose-toxicity model fits
plot-method	Plot the fitted dose-effcacy curve using a model from 'ModelEff' class with samples
plot-method	Plot the fitted dose-DLE curve using a 'ModelTox' class model with samples
plot-method	Plot summaries of the model-based design simulations
plot.gtable	Plots gtable objects
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
plotDualResponses-method	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
plotGain-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
prob	Compute the probability for a given dose, given model and samples
prob-method	Compute the probability for a given dose, given model and samples
probit	Shorthand for probit function
ProbitLogNormal	Initialization function for the "ProbitLogNormal" class
ProbitLogNormal-class	Probit model with bivariate log normal prior
PseudoDualFlexiSimulations	Initialization function for 'PseudoDualFlexiSimulations' class
PseudoDualFlexiSimulations-class	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.
PseudoDualSimulations	Initialization function for 'DualPseudoSimulations' class
PseudoDualSimulations-class	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.
PseudoDualSimulationsSummary-class	Class for the summary of the dual responses simulations using pseudo models
PseudoSimulations	Initialization function of the 'PseudoSimulations' class
PseudoSimulations-class	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'.
PseudoSimulationsSummary-class	Class for the summary of pseudo-models simulations output

-- Q --

Quantiles2LogisticNormal

Convert prior quantiles (lower, median, upper) to logistic (log) normal model

-- R --

Report	A Reference Class to represent sequentially updated reporting objects.
RuleDesign	Initialization function for "RuleDesign"
RuleDesign-class	Class for rule-based designs

-- S --

Samples	Initialization function for "Samples"
Samples-class	Class for the MCMC output
sampleSize	Compute the number of samples for a given MCMC options triple
setSeed	Helper function to set and save the RNG seed
show-method	Show the summary of the dual-endpoint simulations
show-method	Show the summary of the simulations
show-method	Show the summary of Pseudo Dual simulations summary
show-method	Show the summary of the simulations
show-method	Show the summary of the simulations
simulate-method	Simulate outcomes from a CRM design
simulate-method	Simulate outcomes from a dual-endpoint design
simulate-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-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-method	Simulate outcomes from a rule-based design
simulate-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-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
Simulations	Initialization function for the "Simulations" class
Simulations-class	Class for the simulations output from model based designs
SimulationsSummary-class	Class for the summary of model-based simulations output
size	Determine the size of the next cohort
size-method	Determine the size of the next cohort
Stopping-class	The virtual class for stopping rules
StoppingAll	Initialization function for "StoppingAll"
StoppingAll-class	Stop based on fullfillment of all multiple stopping rules
StoppingAny	Initialization function for "StoppingAny"
StoppingAny-class	Stop based on fullfillment of any stopping rule
StoppingCohortsNearDose	Initialization function for "StoppingCohortsNearDose"
StoppingCohortsNearDose-class	Stop based on number of cohorts near to next best dose
StoppingGstarCIRatio	Initialization function for "StoppingGstarCIRatio"
StoppingGstarCIRatio-class	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.
StoppingHighestDose	Initialization function for "StoppingHighestDose"
StoppingHighestDose-class	Stop when the highest dose is reached
StoppingList	Initialization function for "StoppingList"
StoppingList-class	Stop based on multiple stopping rules
StoppingMinCohorts	Initialization function for "StoppingMinCohorts"
StoppingMinCohorts-class	Stop based on minimum number of cohorts
StoppingMinPatients	Initialization function for "StoppingMinPatients"
StoppingMinPatients-class	Stop based on minimum number of patients
StoppingMTDdistribution	Initialization function for "StoppingMTDdistribution"
StoppingMTDdistribution-class	Stop based on MTD distribution
StoppingPatientsNearDose	Initialization function for "StoppingPatientsNearDose"
StoppingPatientsNearDose-class	Stop based on number of patients near to next best dose
StoppingTargetBiomarker	Initialization function for "StoppingTargetBiomarker"
StoppingTargetBiomarker-class	Stop based on probability of target biomarker
StoppingTargetProb	Initialization function for "StoppingTargetProb"
StoppingTargetProb-class	Stop based on probability of target tox interval
StoppingTDCIRatio	Initialization function for "StoppingTDCIRatio"
StoppingTDCIRatio-class	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
stopTrial	Stop the trial?
stopTrial-method	Stop the trial?
summary-method	Summarize the dual-endpoint design simulations, relative to given true dose-toxicity and dose-biomarker curves
summary-method	Summarize the simulations, relative to a given truth
summary-method	Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model.
summary-method	Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model)
summary-method	Summarize the simulations, relative to a given truth
summary-method	Summarize the model-based design simulations, relative to a given truth

-- T --

TDDesign	Initialization function for 'TDDesign' class
TDDesign-class	Design class using DLE responses only based on the pseudo DLE model without sample
TDsamplesDesign	Initialization function for 'TDsamplesDesign' class
TDsamplesDesign-class	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:
ThreePlusThreeDesign	Creates a new 3+3 design object from a dose grid

-- U --

update-method	Update method for the "Data" class
update-method	Update method for the "DataDual" class
update-method	Update method for the "DataParts" class
update-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-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-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.

-- V --

Validate

A Reference Class to help programming validation for new S4 classes

-- W --

writeModel

Creating a WinBUGS model file

-- misc --

%~%	Helper function for value matching with tolerance
&-method	The method combining two atomic stopping rules
&-method	The method combining an atomic and a stopping list
&-method	The method combining a stopping list and an atomic
.AllModels	Class for All models This is a class where all models inherit.
.CohortSizeConst	Constant cohort size
.CohortSizeDLT	Cohort size based on number of DLTs
.CohortSizeMax	Size based on maximum of multiple cohort size rules
.CohortSizeMin	Size based on minimum of multiple cohort size rules
.CohortSizeParts	Cohort size based on the parts
.CohortSizeRange	Cohort size based on dose range
.Data	Class for the data input
.DataDual	Class for the dual endpoint data input
.DataMixture	Class for the data with mixture sharing
.DataParts	Class for the data with two study parts
.Design	Class for the CRM design
.DualDesign	Class for the dual-endpoint CRM design
.DualEndpoint	General class for the dual endpoint model
.DualEndpointBeta	Dual endpoint model with beta function for dose-biomarker relationship
.DualEndpointEmax	Dual endpoint model with emax function for dose-biomarker relationship
.DualEndpointRW	Dual endpoint model with RW prior for biomarker
.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
.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
.DualSimulations	Class for the simulations output from dual-endpoint model based designs
.DualSimulationsSummary	Class for the summary of dual-endpoint simulations output
.EffFlexi	Class for the efficacy model in flexible form for prior expressed in form of pseudo data
.Effloglog	Class for the linear log-log efficacy model using pseudo data prior
.GeneralData	Class for general data input
.GeneralModel	No Intitialization function for this General class for model input
.GeneralSimulations	General class for the simulations output
.GeneralSimulationsSummary	Class for the summary of general simulations output
.IncrementMin	Max increment based on minimum of multiple increment rules
.IncrementsNumDoseLevels	Increments control based on number of dose levels
.IncrementsRelative	Increments control based on relative differences in intervals
.IncrementsRelativeDLT	Increments control based on relative differences in terms of DLTs
.IncrementsRelativeParts	Increments control based on relative differences in intervals, with special rules for part 1 and beginning of part 2
.LogisticIndepBeta	No initialization function Standard logistic model with prior in form of pseudo data
.LogisticKadane	Reparametrized logistic model
.LogisticLogNormal	Standard logistic model with bivariate (log) normal prior
.LogisticLogNormalMixture	Standard logistic model with online mixture of two bivariate log normal priors
.LogisticLogNormalSub	Standard logistic model with bivariate (log) normal prior with substractive dose standardization
.LogisticNormal	Standard logistic model with bivariate normal prior
.LogisticNormalFixedMixture	Standard logistic model with fixed mixture of multiple bivariate (log) normal priors
.LogisticNormalMixture	Standard logistic model with flexible mixture of two bivariate normal priors
.McmcOptions	Class for the three canonical MCMC options
.Model	Class for the model input
.ModelEff	No Initialization function class for Efficacy models using pseudo data prior
.ModelPseudo	Class of models using expressing their prior in form of Pseudo data
.ModelTox	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'
.NextBestDualEndpoint	The class with the input for finding the next dose based on the dual endpoint model
.NextBestMaxGain	Next best dose with maximum gain value based on a pseudo DLE and efficacy model without samples
.NextBestMaxGainSamples	Next best dose with maximum gain value based on a pseudo DLE and efficacy model with samples
.NextBestMTD	The class with the input for finding the next best MTD estimate
.NextBestNCRM	The class with the input for finding the next dose in target interval
.NextBestTD	Next best dose based on Pseudo DLE model without sample
.NextBestTDsamples	Next best dose based on Pseudo DLE Model with samples
.NextBestThreePlusThree	The class with the input for finding the next dose in target interval
.ProbitLogNormal	Probit model with bivariate log normal prior
.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.
.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.
.PseudoDualSimulationsSummary	Class for the summary of the dual responses simulations using pseudo models
.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'.
.PseudoSimulationsSummary	Class for the summary of pseudo-models simulations output
.RuleDesign	Class for rule-based designs
.Samples	Class for the MCMC output
.Simulations	Class for the simulations output from model based designs
.SimulationsSummary	Class for the summary of model-based simulations output
.StoppingAll	Stop based on fullfillment of all multiple stopping rules
.StoppingAny	Stop based on fullfillment of any stopping rule
.StoppingCohortsNearDose	Stop based on number of cohorts near to next best dose
.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.
.StoppingHighestDose	Stop when the highest dose is reached
.StoppingList	Stop based on multiple stopping rules
.StoppingMinCohorts	Stop based on minimum number of cohorts
.StoppingMinPatients	Stop based on minimum number of patients
.StoppingMTDdistribution	Stop based on MTD distribution
.StoppingPatientsNearDose	Stop based on number of patients near to next best dose
.StoppingTargetBiomarker	Stop based on probability of target biomarker
.StoppingTargetProb	Stop based on probability of target tox interval
.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
.TDDesign	Design class using DLE responses only based on the pseudo DLE model without sample
.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:
|-method	The method combining two atomic stopping rules
|-method	The method combining a stopping list and an atomic
|-method	The method combining an atomic and a stopping list