Nothing
Upgrading from crmPack version 1.0"
In crmPack: Object-Oriented Implementation of Dose Escalation Designs
knitr::opts_chunk$set(echo = TRUE)
library(tibble)
library(kableExtra)
library(checkmate)
The following vignette describes the changes that were introduced to the crmPack
package as a result of the package's refactoring. One row in below tables
represents a single-type, consistent change.
Class and slot changes
Naming convention motivation
To be close to common R style guidelines [@advancedR] and use consistent naming conventions within
the crmPack package, CamelCase notation is used for class names, method names and constructor function
names, and snake_case notation is used for slot names throughout the package.
New classes
new_classes <- tribble(
~category, ~class_new_name, ~comment,
"Parent model class", "ModelParamsNormal", "Represents parameters of bivariate normal distribution.",
"Model", "OneParLogNormalPrior",
"standard CRM with a normal prior on the log power parameter for the skeleton prior probabilities",
"Design", "DADesign", "Design for data augmentation.",
"Model", "DALogisticLogNormal", "Logistic model with bivariate (log) normal prior and data augmentation.",
"Data", "DataDA", "Time-to-DLT augmented data.",
"Model", "FractionalCRM", "Fractional CRM model.",
"Increments", "IncrementsHSRBeta", "Limiting further increments using a Hard Safety Rule",
"Increments", "IncrementsRelativeDLTCurrent", "Increments control based on relative differences in terms of DLTs.",
"nextBest", "NextBestEWOC", "Next best dose is the highest possible dose subject to overdose control.",
"nextBest", "NextBestInfTheory", "Next best dose that is based on information theory.",
"nextBest", "NextBestMinDist", "Next best dose that is based on minimum distance to target probability.",
"nextBest", "NextBestNCRMLoss", "Next best dose based on NCRM rule and loss function.",
"nextBest", "NextBestProbMTDLTE", paste(
"Next best dose that selects the dose with the highest probability of having a toxicity",
"rate less or equal to the toxicity target."
),
"nextBest", "NextBestProbMTDMinDist", paste(
"
Next best dose that selects the dose with the highest probability of having a toxicity",
"rate with the smallest distance to the toxicity target."
),
"Model", "OneParExpPrior", "Standard CRM with an exponential prior on the power parameter.",
"Model", "ProbitLogNormalRel",
"Probit regression model with a bivariate normal prior on the intercept and log slope.",
"SafetyWindow", "SafetyWindowConst", "Safety window length used when the `gap` should be kept constant.",
"SafetyWindow", "SafetyWindowSize", "Safety window length based on cohort size.",
"Stopping", "StoppingLowestDoseHSRBeta",
"Stopping based on a Hard Safety Rule using the Beta posterior distribution.",
"Stopping", "StoppingMTDCV",
"Stopping rule based on precision of MTD which is calculated as the coefficient of variation (CV) of the MTD.",
"Stopping", "StoppingMissingDose", "Stopping based on NA returned by next best dose.",
"Stopping", "StoppingSpecificDose",
"Testing a stopping rule at specific dose of the dose grid and not at the next best dose."
)
cn <- new_classes
cn <- cn[order(cn$category, cn$class_new_name), ]
kbl(cn, longtable = TRUE, booktabs = TRUE, col.names = NULL, escape = FALSE) %>%
add_header_above(c("Category", "Name", "Comment")) %>%
column_spec(1:3, width = "6cm", border_left = TRUE, border_right = TRUE) %>%
kable_styling(latex_options = c("repeat_header"), font_size = 11)
Renamed classes
renamed_classes <- tribble(
~category, ~class_new_name, ~class_old_name, ~comment,
"Increments", "IncrementsDoseLevels", "IncrementsNumDoseLevels", "Harmonization",
"Increments", "IncrementsMin", "IncrementMin", "Harmonization",
"Stopping", "StoppingMaxGainCIRatio", "StoppingGstarCIRatio", "Clarification"
)
cr <- renamed_classes
cr <- cr[order(cr$category, cr$class_new_name), ]
kbl(cr, longtable = TRUE, booktabs = TRUE, col.names = NULL, escape = FALSE) %>%
add_header_above(c("Category", "New Name", "Old Name", "Reason")) %>%
column_spec(1:4, width = "6cm", border_left = TRUE, border_right = TRUE) %>%
kable_styling(latex_options = c("repeat_header"), font_size = 11)
Renamed slots
Please note that this list might not be exhaustive. Please always check the relevant class documentation for details.
# "helper", "positive_number", "", "", FALSE, "to handle strictly positive valued slots
# (e.g. `ref_dose` in many model classes)",
# "OneParLogNormalPrior", "", "", "skel_fun", FALSE, "",
# "OneParLogNormalPrior", "", "", "skel_fun_inv", FALSE, "",
# "OneParLogNormalPrior", "", "", "skel_probs", FALSE, "",
# "OneParLogNormalPrior()", "", "", "dose_grid", FALSE, "",
# "Model", "ModelLogNormal", "",
# "Parent model class", "ModelLogNormal", "new parent class for all the models with reference dose and
# bivariate (log) normal prior on the model parameters",
class_changes <- tribble(
~class_name, ~slot_name, ~slot_new_name, ~was_removed, ~comment,
"ModelEff", "dose", "", TRUE, "moved to model class method",
"ModelEff", "ExpEff", "", TRUE, "moved to model class method and renamed to `efficacy`",
"Effloglog", "several slots", "several slots", FALSE, "several slot changed, see man page",
"EffFlexi", "several slots", "several slots", FALSE, "several slot changed, see man page",
"EffFlexi()", "smooth", "", TRUE, "replaced by `rw1` flag",
"EffFlexi()", "rw1", "", FALSE, "replaces `smooth` string",
"DALogisticLogNormal", "C_par", "c_par", FALSE, "",
"DALogisticLogNormal", "conditionalPEM", "cond_pem", FALSE, "",
"TITELogisticLogNormal", "weightMethod", "weight_method", FALSE, "",
"DualEndpointBeta", "refDoseBeta", "ref_dose_beta", FALSE, "",
"DualEndpointEmax", "refDoseEmax", "ref_dose_emax", FALSE, "",
"DualEndpointRW", "useRW1", "rw1", FALSE, "",
"DualEndpointRW()", "smooth", "", TRUE, "replaced by `rw1` flag",
"DualEndpointRW()", "", "rw1", FALSE, "replaces `smooth` string",
"McmcOptions", "", "rng_kind", FALSE, "to be used by Random Number Generator in rJAGS",
"McmcOptions", "", "rng_seed", FALSE, "to be used by Random Number Generator in rJAGS",
"NextBestTD", "targetDuringTrial", "prob_target_drt", FALSE, "",
"NextBestTD", "targetEndOfTrial", "prob_target_eot", FALSE, "",
"NextBestTDsamples", "targetDuringTrial", "prob_target_drt", FALSE, "",
"NextBestTDsamples", "targetEndOfTrial", "prob_target_eot", FALSE, "",
"NextBestNCRM", "maxOverdoseProb", "max_overdose_prob", FALSE, "",
"NextBestDualEndpoint", "scale", "", TRUE, "replaced by `target_relative` flag",
"NextBestDualEndpoint", "", "target_relative", FALSE, "replaces `scale` string",
"NextBestDualEndpoint", "maxOverdoseProb", "max_overdose_prob", FALSE, "",
"NextBestDualEndpoint", "targetThresh", "target_thresh", FALSE, "",
"NextBestMaxGain", "DLEDuringTrialtarget", "prob_target_drt", FALSE, "",
"NextBestMaxGain", "DLEEndOfTrialtarget", "prob_target_eot", FALSE, "",
"NextBestMaxGainSamples", "DLEDuringTrialtarget", "prob_target_drt", FALSE, "",
"NextBestMaxGainSamples", "DLEEndOfTrialtarget", "prob_target_eot", FALSE, "",
"NextBestMaxGainSamples", "TDderive", "derive", FALSE, "",
"NextBestMaxGainSamples", "Gstarderive", "mg_derive", FALSE, "",
"IncrementsRelativeParts", "dltStart", "dlt_start", FALSE, "",
"IncrementsRelativeParts", "cleanStart", "clean_start", FALSE, "change applied also to other dependent classes",
"IncrementsRelativeDLT", "DLTintervals", "dlt_intervals", FALSE, "change applied also to other dependent classes",
"IncrementsDoseLevels (IncrementsNumDoseLevels)", "maxLevels", "levels", FALSE,
"change applied also to other dependent classes",
"IncrementsDoseLevels (IncrementsNumDoseLevels)", "", "basis_level", FALSE, "",
"IncrementsMin (IncrementMin)", "IncrementsList",
"increments_list", FALSE, "change applied also to other dependent classes",
"StoppingTargetBiomarker", "scale", "", TRUE, "replaced by `is_relative` flag",
"StoppingTargetBiomarker", "", "is_relative", FALSE, "replaces `scale` string",
"StoppingList", "stopList", "stop_list", FALSE, "",
"StoppingAll", "stopList", "stop_list", FALSE, "",
"StoppingAny", "stopList", "stop_list", FALSE, "",
"StoppingTDCIRatio", "targetRatio", "target_ratio", FALSE, "",
"StoppingTDCIRatio", "targetEndOfTrial", "prob_target", FALSE, "",
"StoppingMaxGainCIRatio (StoppingGstarCIRatio)", "targetRatio", "target_ratio", FALSE, "",
"StoppingMaxGainCIRatio (StoppingGstarCIRatio)", "targetEndOfTrial", "prob_target", FALSE, "",
"CohortSizeRange", "cohortSize", "cohort_size", FALSE, "",
"CohortSizeDLT", "DLTintervals", "dlt_intervals", FALSE, "",
"CohortSizeDLT", "cohortSize", "cohort_size", FALSE, "",
"CohortSizeMax", "cohortSizeList", "cohort_size_list", FALSE, "",
"CohortSizeMin", "cohortSizeList", "cohort_size_list", FALSE, "",
"Design", "PLcohortSize", "pl_cohort_size", FALSE, "",
"TDsamplesDesign", "PLcohortSize", "pl_cohort_size", FALSE, "",
"TDDesign", "PLcohortSize", "pl_cohort_size", FALSE, ""
)
cc <- class_changes
cc <- cc[order(cc$class_name, cc$slot_name, cc$slot_new_name), ]
no_slot_change <- nchar(trimws(cc$slot_name)) == 0
cc$class_name <- cell_spec(
cc$class_name,
strikeout = ifelse(cc$was_removed & no_slot_change, TRUE, FALSE)
)
cc$slot_name <- cell_spec(
cc$slot_name,
strikeout = ifelse(cc$was_removed & !no_slot_change, TRUE, FALSE)
)
cc <- cc[, -4]
kbl(cc, longtable = TRUE, booktabs = TRUE, col.names = NULL, escape = FALSE) %>%
add_header_above(c("Name", "Name", "New Name", "")) %>%
add_header_above(c("Class/Constructor[note]" = 1, "Slot/Arg[note]" = 2, "Comment")) %>%
column_spec(1:4, width = "6cm", border_left = TRUE, border_right = TRUE) %>%
collapse_rows(columns = 1, valign = "top") %>%
kable_styling(latex_options = c("repeat_header"), font_size = 11) %>%
add_footnote(
c(
"Class or class' user constructor. In the later case the Name/New Name is followed by ().",
"Slot in case of the class or argument in case of the class' user constructor"
),
notation = "symbol"
)
Strikeout indicates that the class/slot was removed.
Moved dose and prob Functions from Slots to Methods
Moved dose and prob functions from model class slots to model class methods.
Example of usage: dose/prob function as a true dose-DLT/DLT-dose relationship.
Generate data, define a model and get samples
library(crmPack)
empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))
my_model <- LogisticNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
)
my_options <- McmcOptions(burnin = 2, step = 2, samples = 20)
my_samples <- mcmc(empty_data, my_model, my_options)
Dose
Here is the example on how the dose function can be used in case of different
inputs, i.e. model's parameters samples or in case of a fixed model's parameters
values.
# Doses reaching a specific target probability of the occurrence of a DLT (equal to 0.3),
# given a particular models and samples.
# Every single dose corresponds to one particular sample in `my_samples`.
dose(0.3, my_model, my_samples)
# True dose-DLT relationship.
# Say that -0.8 and 1 are the true values for models parameters alpha0 and alpha1 respectively.
# The `true_dose_fun` takes one argument (target probability of the occurrence of a DLT)
# and computes the corresponding dose, according to the model chosen and given a fixed values
# of the model's parameters.
true_dose_fun <- doseFunction(my_model, alpha0 = -0.8, alpha1 = 1)
true_dose_fun(0.3)
Prob
# Toxicity probabilities for a given dose (equal to 10), model and samples.
# Every single probability value corresponds to one particular sample in `my_samples`.
prob(10, my_model, my_samples)
# True DLT-dose relationship.
# Say that -0.8 and 1 are the true values for models parameters alpha0 and alpha1 respectively.
# The `true_prob_fun` takes one argument (the dose) and computes the corresponding
# toxicity probability, according to the model chosen and given a fixed values
# of the model's parameters.
true_prob_fun <- probFunction(my_model, alpha0 = -0.8, alpha1 = 1)
true_prob_fun(10)
New Random Number Generator settings for the MCMC
The Random Number Generator (RNG) settings used by the JAGS for the MCMC are now
configured solely through the McmcOptions class. The RNG settings are:
RNG type and the RNG seed that corresponds to a given RNG type. Find out
details in the help page for the McmcOptions class.
Any RNG-related user settings at the R session level (such us those with
set.seed()) are ignored by the MCMC sampler.
New no-argument constructors
To aid software development, new no-argument constructs for all sub-classes of GeneralModel, Increments, NextBest and Stopping have been introduced. The names of these constructors take the form .Default<classname>, where <classname> is the name of the class being created.
These constructors return valid, but not necessarily contextually sensible, objects of the required class. One reason the objects returned may not be contextually sensible is that the constructors take no account of any associated doseGrid.
Here are some examples of their use:
.DefaultStoppingAll()
class_name <- "LogisticNormal"
eval(parse(text = paste0(".Default", class_name, "()")))
Handling of NA or placebo returned as next dose
For consistent handling how the study is stopped and to facilitate analysis of stop reasons in the operation characteristics, the handling of NA and placebo returned by nextBest methods is changed. In the previous version of crmPack stopping for placebo or NA returned by a nextBest method was handled automatically in the generic Stopping method. This is now moved into a new stopping rule StoppingMissingDose. As a consequence, the stopping rule StoppingMissingDose must be specified for those nextBest methods that can return NA, or when placebo is used. Otherwise the simulation may run into an error if the study is not stopped when NA is returned as the next dose. nextBest methods that can return NA are NextBestNCRM, NextBestNCRMLoss and NextBestDualEndpoint.
Evaluation of stopping rules at a specific dose
Without further specification, stopping rules are evaluated at the dose returned by the used nextBest method. With the new stopping rule StoppingSpecificDose it is possible to evaluate stopping rules at any dose. For usage see documentation of StoppingSpecificDose.
Further details in class and methods name changes
Classes
class_changes <- tribble(
~class_name, ~class_new_name, ~slot_name, ~slot_new_name, ~was_removed, ~comment,
"", "positive_number", "", "", FALSE, "to handle strictly positive valued slots
(e.g. `ref_dose` in many model classes)",
"", "ModelLogNormal", "", "", FALSE, "new parent class for all the models with reference dose and
bivariate (log) normal prior on the model parameters",
"", "ModelParamsNormal", "", "", FALSE, "represents parameters of bivariate normal distribution",
"AllModels", "", "", "", TRUE, "",
"Model", "", "", "", TRUE, "",
"GeneralModel", "", "", "datanames", FALSE, "moved from `AllModels`",
"GeneralModel", "", "", "datanames_prior", FALSE, "",
"ModelEff", "", "dose", "", TRUE, "moved to model class method",
"ModelEff", "", "ExpEff", "", TRUE, "moved to model class method and renamed to `efficacy`",
"Effloglog", "", "several slots", "several slots", FALSE, "several slot changed, see man page",
"EffFlexi", "", "several slots", "several slots", FALSE, "several slot changed, see man page",
"EffFlexi()", "", "smooth", "", TRUE, "replaced by `rw1` flag",
"EffFlexi()", "", "", "rw1", FALSE, "replaces `smooth` string",
"DALogisticLogNormal", "", "C_par", "c_par", FALSE, "",
"DALogisticLogNormal", "", "conditionalPEM", "cond_pem", FALSE, "",
"TITELogisticLogNormal", "", "weightMethod", "weight_method", FALSE, "",
"OneParLogNormalPrior", "", "skeletonFun", "skel_fun", FALSE, "",
"OneParLogNormalPrior", "", "", "skel_fun_inv", FALSE, "",
"OneParLogNormalPrior", "", "skeletonProbs", "skel_probs", FALSE, "",
"OneParLogNormalPrior()", "", "doseGrid", "dose_grid", FALSE, "",
"DualEndpointBeta", "", "refDoseBeta", "ref_dose_beta", FALSE, "",
"DualEndpointEmax", "", "refDoseEmax", "ref_dose_emax", FALSE, "",
"DualEndpointRW", "", "useRW1", "rw1", FALSE, "",
"DualEndpointRW()", "", "smooth", "", TRUE, "replaced by `rw1` flag",
"DualEndpointRW()", "", "", "rw1", FALSE, "replaces `smooth` string",
"McmcOptions", "", "", "rng_kind", FALSE, "to be used by Random Number Generator in rJAGS",
"McmcOptions", "", "", "rng_seed", FALSE, "to be used by Random Number Generator in rJAGS",
"NextBestTD", "", "targetDuringTrial", "prob_target_drt", FALSE, "",
"NextBestTD", "", "targetEndOfTrial", "prob_target_eot", FALSE, "",
"NextBestTDsamples", "", "targetDuringTrial", "prob_target_drt", FALSE, "",
"NextBestTDsamples", "", "targetEndOfTrial", "prob_target_eot", FALSE, "",
"NextBestNCRM", "", "maxOverdoseProb", "max_overdose_prob", FALSE, "",
"NextBestDualEndpoint", "", "scale", "", TRUE, "replaced by `target_relative` flag",
"NextBestDualEndpoint", "", "", "target_relative", FALSE, "replaces `scale` string",
"NextBestDualEndpoint", "", "maxOverdoseProb", "max_overdose_prob", FALSE, "",
"NextBestDualEndpoint", "", "targetThresh", "target_thresh", FALSE, "",
"NextBestMaxGain", "", "DLEDuringTrialtarget", "prob_target_drt", FALSE, "",
"NextBestMaxGain", "", "DLEEndOfTrialtarget", "prob_target_eot", FALSE, "",
"NextBestMaxGainSamples", "", "DLEDuringTrialtarget", "prob_target_drt", FALSE, "",
"NextBestMaxGainSamples", "", "DLEEndOfTrialtarget", "prob_target_eot", FALSE, "",
"NextBestMaxGainSamples", "", "TDderive", "derive", FALSE, "",
"NextBestMaxGainSamples", "", "Gstarderive", "mg_derive", FALSE, "",
"IncrementsRelativeParts", "", "dltStart", "dlt_start", FALSE, "",
"IncrementsRelativeParts", "", "cleanStart", "clean_start", FALSE, "change applied also to other dependent classes",
"IncrementsRelativeDLT", "", "DLTintervals", "intervals", FALSE, "change applied also to other dependent classes",
"IncrementsNumDoseLevels", "IncrementsDoseLevels", "", "", FALSE, "",
"IncrementsDoseLevels", "", "maxLevels", "levels", FALSE, "change applied also to other dependent classes",
"IncrementsDoseLevels", "", "", "basis_level", FALSE, "",
"IncrementMin", "IncrementsMin", "", "", FALSE, "",
"IncrementsMin", "", "IncrementsList", "increments_list", FALSE, "change applied also to other dependent classes",
"StoppingTargetBiomarker", "", "scale", "", TRUE, "replaced by `is_relative` flag",
"StoppingTargetBiomarker", "", "", "is_relative", FALSE, "replaces `scale` string",
"StoppingList", "", "stopList", "stop_list", FALSE, "",
"StoppingAll", "", "stopList", "stop_list", FALSE, "",
"StoppingAny", "", "stopList", "stop_list", FALSE, "",
"StoppingTDCIRatio", "", "targetRatio", "target_ratio", FALSE, "",
"StoppingTDCIRatio", "", "targetEndOfTrial", "prob_target", FALSE, "",
"StoppingGstarCIRatio", "", "targetRatio", "target_ratio", FALSE, "",
"StoppingGstarCIRatio", "", "targetEndOfTrial", "prob_target", FALSE, "",
"StoppingGstarCIRatio", "StoppingMaxGainCIRatio", "", "", FALSE, "",
"CohortSizeRange", "", "cohortSize", "cohort_size", FALSE, "",
"CohortSizeDLT", "", "DLTintervals", "intervals", FALSE, "",
"CohortSizeDLT", "", "cohortSize", "cohort_size", FALSE, "",
"CohortSizeMax", "", "cohortSizeList", "cohort_sizes", FALSE, "",
"CohortSizeMin", "", "cohortSizeList", "cohort_sizes", FALSE, "",
"SafetyWindowSize", "", "patientGap", "gap", FALSE, "",
"SafetyWindowSize", "", "sizeIntervals", "size", FALSE, "",
"SafetyWindowSize", "", "patientFollow", "follow", FALSE, "",
"SafetyWindowSize", "", "patientFollowMin", "follow_min", FALSE, "",
"SafetyWindowConst", "", "patientGap", "gap", FALSE, "",
"SafetyWindowConst", "", "patientFollow", "follow", FALSE, "",
"SafetyWindowConst", "", "patientFollowMin", "follow_min", FALSE, "",
"OneParExpNormalPrior", "OneParLogNormalPrior", "", "", FALSE, "",
"Design", "", "PLcohortSize", "pl_cohort_size", FALSE, "",
"TDsamplesDesign", "", "PLcohortSize", "pl_cohort_size", FALSE, "",
"TDDesign", "", "PLcohortSize", "pl_cohort_size", FALSE, ""
)
cc <- class_changes
cc <- cc[order(cc$class_name, cc$class_new_name, cc$slot_name, cc$slot_new_name), ]
no_slot_change <- nchar(trimws(cc$slot_name)) == 0
cc$class_name <- cell_spec(
cc$class_name,
strikeout = ifelse(cc$was_removed & no_slot_change, TRUE, FALSE)
)
cc$slot_name <- cell_spec(
cc$slot_name,
strikeout = ifelse(cc$was_removed & !no_slot_change, TRUE, FALSE)
)
cc <- cc[, -5]
kbl(cc, longtable = TRUE, booktabs = TRUE, col.names = NULL, escape = FALSE) %>%
add_header_above(c("Name", "New Name", "Name", "New Name", "")) %>%
add_header_above(c("Class/Constructor[note]" = 2, "Slot/Arg[note]" = 2, "Comment")) %>%
column_spec(1:5, width = "6cm", border_left = TRUE, border_right = TRUE) %>%
collapse_rows(columns = 1, valign = "top") %>%
kable_styling(latex_options = c("repeat_header"), font_size = 11) %>%
add_footnote(
c(
"Class or class' user constructor. In the later case the Name/New Name is followed by ().",
"Slot in case of the class or argument in case of the class' user constructor"
),
notation = "symbol"
)
Strikeout indicates that the class/slot was removed.
Methods
method_changes <- tribble(
~method_name, ~method_new_name, ~arg_name, ~arg_new_name, ~was_removed, ~output_changes, ~comment,
"update-Data", "", "newCohort", "new_cohort", FALSE, "", "",
"update-Data", "", "", "check", FALSE, "", "",
"update-DataParts", "", "", "check", FALSE, "", "",
"update-DataDual", "", "", "check", FALSE, "", "",
"update-DataDA", "", "", "check", FALSE, "", "",
"getEff", "", "", "", FALSE, "renamed elements in the list returned", "",
"mcmc", "", "fromPrior", "from_prior", FALSE, "", "",
"mcmc", "", "program", "", TRUE, "", "",
"mcmc", "", "verbose", "", TRUE, "", "",
"", "doseFunction", "", "", FALSE, "", "returns dose computing function for a given model",
"", "probFunction", "", "", FALSE, "", "returns probability computing function for a given model",
"dose", "", "prob", "x", FALSE, "", "",
"", "efficacy", "", "", FALSE, "", "new generic function",
"biomLevel", "biomarker", "", "", FALSE, "", "changed generic and methods",
"biomLevel", "", "dose", "", TRUE, "", "changed generic and methods",
"biomLevel-DualEndpoint", "", "dose", "", TRUE, "", "",
"biomLevel", "", "", "xLevel", FALSE, "", "added only for generic, reordered arguments for generic and methods",
"gain", "", "several arguments", "several arguments", FALSE, "", "several arguments (generic and methods) changed,
see man page",
"", "update-ModelPseudo", "", "", FALSE, "", "",
"update-LogisticIndepBeta", "", "", "", TRUE, "", "replaced by `update-ModelPseudo`",
"update-Effloglog", "", "", "", TRUE, "", "replaced by `update-ModelPseudo`",
"update-EffFlexi", "", "", "", TRUE, "", "replaced by `update-ModelPseudo`",
"nextBest-NextBestTD", "", "Effmodel", "model_eff", FALSE, "", "",
"nextBest-NextBestTD", "", "Effsamples", "samples_eff", FALSE, "", "",
"nextBest-NextBestTD", "", "SIM", "in_sim", FALSE, "", "",
"nextBest-NextBestTDsamples", "", "Effmodel", "model_eff", FALSE, "", "",
"nextBest-NextBestTDsamples", "", "Effsamples", "samples_eff", FALSE, "", "",
"nextBest-NextBestMaxGain", "", "Effmodel", "model_eff", FALSE, "", "",
"nextBest-NextBestMaxGain", "", "Effsamples", "samples_eff", FALSE, "", "",
"nextBest-NextBestMaxGain", "", "SIM", "in_sim", FALSE, "", "",
"nextBest-NextBestMaxGain", "", "", "", FALSE, "renamed elements in the list returned", "",
"nextBest-NextBestMaxGainSamples", "", "Effmodel", "model_eff", FALSE, "", "",
"nextBest-NextBestMaxGainSamples", "", "Effsamples", "samples_eff", FALSE, "", "",
"nextBest-NextBestMaxGainSamples", "", "SIM", "in_sim", FALSE, "", "",
"nextBest-NextBestMaxGainSamples", "", "", "", FALSE, "renamed elements in the list returned", "",
"size", "size", "", "check", FALSE, "", ""
)
mc <- method_changes
mc <- mc[order(mc$method_name, mc$method_new_name, mc$arg_name, mc$arg_new_name), ]
no_arg_change <- nchar(trimws(mc$arg_name)) == 0
mc$method_name <- cell_spec(
mc$method_name,
strikeout = ifelse(mc$was_removed & no_arg_change, TRUE, FALSE)
)
mc$arg_name <- cell_spec(
mc$arg_name,
strikeout = ifelse(mc$was_removed & !no_arg_change, TRUE, FALSE)
)
mc <- mc[, -5]
kbl(mc, longtable = TRUE, booktabs = TRUE, col.names = NULL, escape = FALSE) %>%
add_header_above(c("Name", "New Name", "Name", "New Name", "", "")) %>%
add_header_above(c("Method" = 2, "Argument" = 2, "Output Changes", "Comment")) %>%
column_spec(1:6, width = "6cm", border_left = TRUE, border_right = TRUE) %>%
collapse_rows(columns = 1, valign = "top") %>%
kable_styling(latex_options = c("repeat_header"), font_size = 11)
Strikeout indicates that the method/argument was removed.
References
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knitr::opts_chunk$set(echo = TRUE) library(tibble) library(kableExtra) library(checkmate)
The following vignette describes the changes that were introduced to the crmPack
package as a result of the package's refactoring. One row in below tables
represents a single-type, consistent change.
Class and slot changes
Naming convention motivation
To be close to common R style guidelines [@advancedR] and use consistent naming conventions within
the crmPack package, CamelCase notation is used for class names, method names and constructor function
names, and snake_case notation is used for slot names throughout the package.
New classes
new_classes <- tribble( ~category, ~class_new_name, ~comment, "Parent model class", "ModelParamsNormal", "Represents parameters of bivariate normal distribution.", "Model", "OneParLogNormalPrior", "standard CRM with a normal prior on the log power parameter for the skeleton prior probabilities", "Design", "DADesign", "Design for data augmentation.", "Model", "DALogisticLogNormal", "Logistic model with bivariate (log) normal prior and data augmentation.", "Data", "DataDA", "Time-to-DLT augmented data.", "Model", "FractionalCRM", "Fractional CRM model.", "Increments", "IncrementsHSRBeta", "Limiting further increments using a Hard Safety Rule", "Increments", "IncrementsRelativeDLTCurrent", "Increments control based on relative differences in terms of DLTs.", "nextBest", "NextBestEWOC", "Next best dose is the highest possible dose subject to overdose control.", "nextBest", "NextBestInfTheory", "Next best dose that is based on information theory.", "nextBest", "NextBestMinDist", "Next best dose that is based on minimum distance to target probability.", "nextBest", "NextBestNCRMLoss", "Next best dose based on NCRM rule and loss function.", "nextBest", "NextBestProbMTDLTE", paste( "Next best dose that selects the dose with the highest probability of having a toxicity", "rate less or equal to the toxicity target." ), "nextBest", "NextBestProbMTDMinDist", paste( " Next best dose that selects the dose with the highest probability of having a toxicity", "rate with the smallest distance to the toxicity target." ), "Model", "OneParExpPrior", "Standard CRM with an exponential prior on the power parameter.", "Model", "ProbitLogNormalRel", "Probit regression model with a bivariate normal prior on the intercept and log slope.", "SafetyWindow", "SafetyWindowConst", "Safety window length used when the `gap` should be kept constant.", "SafetyWindow", "SafetyWindowSize", "Safety window length based on cohort size.", "Stopping", "StoppingLowestDoseHSRBeta", "Stopping based on a Hard Safety Rule using the Beta posterior distribution.", "Stopping", "StoppingMTDCV", "Stopping rule based on precision of MTD which is calculated as the coefficient of variation (CV) of the MTD.", "Stopping", "StoppingMissingDose", "Stopping based on NA returned by next best dose.", "Stopping", "StoppingSpecificDose", "Testing a stopping rule at specific dose of the dose grid and not at the next best dose." )
cn <- new_classes cn <- cn[order(cn$category, cn$class_new_name), ] kbl(cn, longtable = TRUE, booktabs = TRUE, col.names = NULL, escape = FALSE) %>% add_header_above(c("Category", "Name", "Comment")) %>% column_spec(1:3, width = "6cm", border_left = TRUE, border_right = TRUE) %>% kable_styling(latex_options = c("repeat_header"), font_size = 11)
Renamed classes
renamed_classes <- tribble( ~category, ~class_new_name, ~class_old_name, ~comment, "Increments", "IncrementsDoseLevels", "IncrementsNumDoseLevels", "Harmonization", "Increments", "IncrementsMin", "IncrementMin", "Harmonization", "Stopping", "StoppingMaxGainCIRatio", "StoppingGstarCIRatio", "Clarification" )
cr <- renamed_classes cr <- cr[order(cr$category, cr$class_new_name), ] kbl(cr, longtable = TRUE, booktabs = TRUE, col.names = NULL, escape = FALSE) %>% add_header_above(c("Category", "New Name", "Old Name", "Reason")) %>% column_spec(1:4, width = "6cm", border_left = TRUE, border_right = TRUE) %>% kable_styling(latex_options = c("repeat_header"), font_size = 11)
Renamed slots
Please note that this list might not be exhaustive. Please always check the relevant class documentation for details.
# "helper", "positive_number", "", "", FALSE, "to handle strictly positive valued slots # (e.g. `ref_dose` in many model classes)", # "OneParLogNormalPrior", "", "", "skel_fun", FALSE, "", # "OneParLogNormalPrior", "", "", "skel_fun_inv", FALSE, "", # "OneParLogNormalPrior", "", "", "skel_probs", FALSE, "", # "OneParLogNormalPrior()", "", "", "dose_grid", FALSE, "", # "Model", "ModelLogNormal", "", # "Parent model class", "ModelLogNormal", "new parent class for all the models with reference dose and # bivariate (log) normal prior on the model parameters", class_changes <- tribble( ~class_name, ~slot_name, ~slot_new_name, ~was_removed, ~comment, "ModelEff", "dose", "", TRUE, "moved to model class method", "ModelEff", "ExpEff", "", TRUE, "moved to model class method and renamed to `efficacy`", "Effloglog", "several slots", "several slots", FALSE, "several slot changed, see man page", "EffFlexi", "several slots", "several slots", FALSE, "several slot changed, see man page", "EffFlexi()", "smooth", "", TRUE, "replaced by `rw1` flag", "EffFlexi()", "rw1", "", FALSE, "replaces `smooth` string", "DALogisticLogNormal", "C_par", "c_par", FALSE, "", "DALogisticLogNormal", "conditionalPEM", "cond_pem", FALSE, "", "TITELogisticLogNormal", "weightMethod", "weight_method", FALSE, "", "DualEndpointBeta", "refDoseBeta", "ref_dose_beta", FALSE, "", "DualEndpointEmax", "refDoseEmax", "ref_dose_emax", FALSE, "", "DualEndpointRW", "useRW1", "rw1", FALSE, "", "DualEndpointRW()", "smooth", "", TRUE, "replaced by `rw1` flag", "DualEndpointRW()", "", "rw1", FALSE, "replaces `smooth` string", "McmcOptions", "", "rng_kind", FALSE, "to be used by Random Number Generator in rJAGS", "McmcOptions", "", "rng_seed", FALSE, "to be used by Random Number Generator in rJAGS", "NextBestTD", "targetDuringTrial", "prob_target_drt", FALSE, "", "NextBestTD", "targetEndOfTrial", "prob_target_eot", FALSE, "", "NextBestTDsamples", "targetDuringTrial", "prob_target_drt", FALSE, "", "NextBestTDsamples", "targetEndOfTrial", "prob_target_eot", FALSE, "", "NextBestNCRM", "maxOverdoseProb", "max_overdose_prob", FALSE, "", "NextBestDualEndpoint", "scale", "", TRUE, "replaced by `target_relative` flag", "NextBestDualEndpoint", "", "target_relative", FALSE, "replaces `scale` string", "NextBestDualEndpoint", "maxOverdoseProb", "max_overdose_prob", FALSE, "", "NextBestDualEndpoint", "targetThresh", "target_thresh", FALSE, "", "NextBestMaxGain", "DLEDuringTrialtarget", "prob_target_drt", FALSE, "", "NextBestMaxGain", "DLEEndOfTrialtarget", "prob_target_eot", FALSE, "", "NextBestMaxGainSamples", "DLEDuringTrialtarget", "prob_target_drt", FALSE, "", "NextBestMaxGainSamples", "DLEEndOfTrialtarget", "prob_target_eot", FALSE, "", "NextBestMaxGainSamples", "TDderive", "derive", FALSE, "", "NextBestMaxGainSamples", "Gstarderive", "mg_derive", FALSE, "", "IncrementsRelativeParts", "dltStart", "dlt_start", FALSE, "", "IncrementsRelativeParts", "cleanStart", "clean_start", FALSE, "change applied also to other dependent classes", "IncrementsRelativeDLT", "DLTintervals", "dlt_intervals", FALSE, "change applied also to other dependent classes", "IncrementsDoseLevels (IncrementsNumDoseLevels)", "maxLevels", "levels", FALSE, "change applied also to other dependent classes", "IncrementsDoseLevels (IncrementsNumDoseLevels)", "", "basis_level", FALSE, "", "IncrementsMin (IncrementMin)", "IncrementsList", "increments_list", FALSE, "change applied also to other dependent classes", "StoppingTargetBiomarker", "scale", "", TRUE, "replaced by `is_relative` flag", "StoppingTargetBiomarker", "", "is_relative", FALSE, "replaces `scale` string", "StoppingList", "stopList", "stop_list", FALSE, "", "StoppingAll", "stopList", "stop_list", FALSE, "", "StoppingAny", "stopList", "stop_list", FALSE, "", "StoppingTDCIRatio", "targetRatio", "target_ratio", FALSE, "", "StoppingTDCIRatio", "targetEndOfTrial", "prob_target", FALSE, "", "StoppingMaxGainCIRatio (StoppingGstarCIRatio)", "targetRatio", "target_ratio", FALSE, "", "StoppingMaxGainCIRatio (StoppingGstarCIRatio)", "targetEndOfTrial", "prob_target", FALSE, "", "CohortSizeRange", "cohortSize", "cohort_size", FALSE, "", "CohortSizeDLT", "DLTintervals", "dlt_intervals", FALSE, "", "CohortSizeDLT", "cohortSize", "cohort_size", FALSE, "", "CohortSizeMax", "cohortSizeList", "cohort_size_list", FALSE, "", "CohortSizeMin", "cohortSizeList", "cohort_size_list", FALSE, "", "Design", "PLcohortSize", "pl_cohort_size", FALSE, "", "TDsamplesDesign", "PLcohortSize", "pl_cohort_size", FALSE, "", "TDDesign", "PLcohortSize", "pl_cohort_size", FALSE, "" )
cc <- class_changes cc <- cc[order(cc$class_name, cc$slot_name, cc$slot_new_name), ] no_slot_change <- nchar(trimws(cc$slot_name)) == 0 cc$class_name <- cell_spec( cc$class_name, strikeout = ifelse(cc$was_removed & no_slot_change, TRUE, FALSE) ) cc$slot_name <- cell_spec( cc$slot_name, strikeout = ifelse(cc$was_removed & !no_slot_change, TRUE, FALSE) ) cc <- cc[, -4] kbl(cc, longtable = TRUE, booktabs = TRUE, col.names = NULL, escape = FALSE) %>% add_header_above(c("Name", "Name", "New Name", "")) %>% add_header_above(c("Class/Constructor[note]" = 1, "Slot/Arg[note]" = 2, "Comment")) %>% column_spec(1:4, width = "6cm", border_left = TRUE, border_right = TRUE) %>% collapse_rows(columns = 1, valign = "top") %>% kable_styling(latex_options = c("repeat_header"), font_size = 11) %>% add_footnote( c( "Class or class' user constructor. In the later case the Name/New Name is followed by ().", "Slot in case of the class or argument in case of the class' user constructor" ), notation = "symbol" )
Strikeout indicates that the class/slot was removed.
Moved dose and prob Functions from Slots to Methods
Moved dose and prob functions from model class slots to model class methods.
Example of usage: dose/prob function as a true dose-DLT/DLT-dose relationship.
Generate data, define a model and get samples
library(crmPack) empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100)) my_model <- LogisticNormal( mean = c(-0.85, 1), cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2) ) my_options <- McmcOptions(burnin = 2, step = 2, samples = 20) my_samples <- mcmc(empty_data, my_model, my_options)
Dose
Here is the example on how the dose function can be used in case of different
inputs, i.e. model's parameters samples or in case of a fixed model's parameters
values.
# Doses reaching a specific target probability of the occurrence of a DLT (equal to 0.3), # given a particular models and samples. # Every single dose corresponds to one particular sample in `my_samples`. dose(0.3, my_model, my_samples) # True dose-DLT relationship. # Say that -0.8 and 1 are the true values for models parameters alpha0 and alpha1 respectively. # The `true_dose_fun` takes one argument (target probability of the occurrence of a DLT) # and computes the corresponding dose, according to the model chosen and given a fixed values # of the model's parameters. true_dose_fun <- doseFunction(my_model, alpha0 = -0.8, alpha1 = 1) true_dose_fun(0.3)
Prob
# Toxicity probabilities for a given dose (equal to 10), model and samples. # Every single probability value corresponds to one particular sample in `my_samples`. prob(10, my_model, my_samples) # True DLT-dose relationship. # Say that -0.8 and 1 are the true values for models parameters alpha0 and alpha1 respectively. # The `true_prob_fun` takes one argument (the dose) and computes the corresponding # toxicity probability, according to the model chosen and given a fixed values # of the model's parameters. true_prob_fun <- probFunction(my_model, alpha0 = -0.8, alpha1 = 1) true_prob_fun(10)
New Random Number Generator settings for the MCMC
The Random Number Generator (RNG) settings used by the JAGS for the MCMC are now
configured solely through the McmcOptions class. The RNG settings are:
RNG type and the RNG seed that corresponds to a given RNG type. Find out
details in the help page for the McmcOptions class.
Any RNG-related user settings at the R session level (such us those with
set.seed()) are ignored by the MCMC sampler.
New no-argument constructors
To aid software development, new no-argument constructs for all sub-classes of GeneralModel, Increments, NextBest and Stopping have been introduced. The names of these constructors take the form .Default<classname>, where <classname> is the name of the class being created.
These constructors return valid, but not necessarily contextually sensible, objects of the required class. One reason the objects returned may not be contextually sensible is that the constructors take no account of any associated doseGrid.
Here are some examples of their use:
.DefaultStoppingAll() class_name <- "LogisticNormal" eval(parse(text = paste0(".Default", class_name, "()")))
Handling of NA or placebo returned as next dose
For consistent handling how the study is stopped and to facilitate analysis of stop reasons in the operation characteristics, the handling of NA and placebo returned by nextBest methods is changed. In the previous version of crmPack stopping for placebo or NA returned by a nextBest method was handled automatically in the generic Stopping method. This is now moved into a new stopping rule StoppingMissingDose. As a consequence, the stopping rule StoppingMissingDose must be specified for those nextBest methods that can return NA, or when placebo is used. Otherwise the simulation may run into an error if the study is not stopped when NA is returned as the next dose. nextBest methods that can return NA are NextBestNCRM, NextBestNCRMLoss and NextBestDualEndpoint.
Evaluation of stopping rules at a specific dose
Without further specification, stopping rules are evaluated at the dose returned by the used nextBest method. With the new stopping rule StoppingSpecificDose it is possible to evaluate stopping rules at any dose. For usage see documentation of StoppingSpecificDose.
Further details in class and methods name changes
Classes
class_changes <- tribble( ~class_name, ~class_new_name, ~slot_name, ~slot_new_name, ~was_removed, ~comment, "", "positive_number", "", "", FALSE, "to handle strictly positive valued slots (e.g. `ref_dose` in many model classes)", "", "ModelLogNormal", "", "", FALSE, "new parent class for all the models with reference dose and bivariate (log) normal prior on the model parameters", "", "ModelParamsNormal", "", "", FALSE, "represents parameters of bivariate normal distribution", "AllModels", "", "", "", TRUE, "", "Model", "", "", "", TRUE, "", "GeneralModel", "", "", "datanames", FALSE, "moved from `AllModels`", "GeneralModel", "", "", "datanames_prior", FALSE, "", "ModelEff", "", "dose", "", TRUE, "moved to model class method", "ModelEff", "", "ExpEff", "", TRUE, "moved to model class method and renamed to `efficacy`", "Effloglog", "", "several slots", "several slots", FALSE, "several slot changed, see man page", "EffFlexi", "", "several slots", "several slots", FALSE, "several slot changed, see man page", "EffFlexi()", "", "smooth", "", TRUE, "replaced by `rw1` flag", "EffFlexi()", "", "", "rw1", FALSE, "replaces `smooth` string", "DALogisticLogNormal", "", "C_par", "c_par", FALSE, "", "DALogisticLogNormal", "", "conditionalPEM", "cond_pem", FALSE, "", "TITELogisticLogNormal", "", "weightMethod", "weight_method", FALSE, "", "OneParLogNormalPrior", "", "skeletonFun", "skel_fun", FALSE, "", "OneParLogNormalPrior", "", "", "skel_fun_inv", FALSE, "", "OneParLogNormalPrior", "", "skeletonProbs", "skel_probs", FALSE, "", "OneParLogNormalPrior()", "", "doseGrid", "dose_grid", FALSE, "", "DualEndpointBeta", "", "refDoseBeta", "ref_dose_beta", FALSE, "", "DualEndpointEmax", "", "refDoseEmax", "ref_dose_emax", FALSE, "", "DualEndpointRW", "", "useRW1", "rw1", FALSE, "", "DualEndpointRW()", "", "smooth", "", TRUE, "replaced by `rw1` flag", "DualEndpointRW()", "", "", "rw1", FALSE, "replaces `smooth` string", "McmcOptions", "", "", "rng_kind", FALSE, "to be used by Random Number Generator in rJAGS", "McmcOptions", "", "", "rng_seed", FALSE, "to be used by Random Number Generator in rJAGS", "NextBestTD", "", "targetDuringTrial", "prob_target_drt", FALSE, "", "NextBestTD", "", "targetEndOfTrial", "prob_target_eot", FALSE, "", "NextBestTDsamples", "", "targetDuringTrial", "prob_target_drt", FALSE, "", "NextBestTDsamples", "", "targetEndOfTrial", "prob_target_eot", FALSE, "", "NextBestNCRM", "", "maxOverdoseProb", "max_overdose_prob", FALSE, "", "NextBestDualEndpoint", "", "scale", "", TRUE, "replaced by `target_relative` flag", "NextBestDualEndpoint", "", "", "target_relative", FALSE, "replaces `scale` string", "NextBestDualEndpoint", "", "maxOverdoseProb", "max_overdose_prob", FALSE, "", "NextBestDualEndpoint", "", "targetThresh", "target_thresh", FALSE, "", "NextBestMaxGain", "", "DLEDuringTrialtarget", "prob_target_drt", FALSE, "", "NextBestMaxGain", "", "DLEEndOfTrialtarget", "prob_target_eot", FALSE, "", "NextBestMaxGainSamples", "", "DLEDuringTrialtarget", "prob_target_drt", FALSE, "", "NextBestMaxGainSamples", "", "DLEEndOfTrialtarget", "prob_target_eot", FALSE, "", "NextBestMaxGainSamples", "", "TDderive", "derive", FALSE, "", "NextBestMaxGainSamples", "", "Gstarderive", "mg_derive", FALSE, "", "IncrementsRelativeParts", "", "dltStart", "dlt_start", FALSE, "", "IncrementsRelativeParts", "", "cleanStart", "clean_start", FALSE, "change applied also to other dependent classes", "IncrementsRelativeDLT", "", "DLTintervals", "intervals", FALSE, "change applied also to other dependent classes", "IncrementsNumDoseLevels", "IncrementsDoseLevels", "", "", FALSE, "", "IncrementsDoseLevels", "", "maxLevels", "levels", FALSE, "change applied also to other dependent classes", "IncrementsDoseLevels", "", "", "basis_level", FALSE, "", "IncrementMin", "IncrementsMin", "", "", FALSE, "", "IncrementsMin", "", "IncrementsList", "increments_list", FALSE, "change applied also to other dependent classes", "StoppingTargetBiomarker", "", "scale", "", TRUE, "replaced by `is_relative` flag", "StoppingTargetBiomarker", "", "", "is_relative", FALSE, "replaces `scale` string", "StoppingList", "", "stopList", "stop_list", FALSE, "", "StoppingAll", "", "stopList", "stop_list", FALSE, "", "StoppingAny", "", "stopList", "stop_list", FALSE, "", "StoppingTDCIRatio", "", "targetRatio", "target_ratio", FALSE, "", "StoppingTDCIRatio", "", "targetEndOfTrial", "prob_target", FALSE, "", "StoppingGstarCIRatio", "", "targetRatio", "target_ratio", FALSE, "", "StoppingGstarCIRatio", "", "targetEndOfTrial", "prob_target", FALSE, "", "StoppingGstarCIRatio", "StoppingMaxGainCIRatio", "", "", FALSE, "", "CohortSizeRange", "", "cohortSize", "cohort_size", FALSE, "", "CohortSizeDLT", "", "DLTintervals", "intervals", FALSE, "", "CohortSizeDLT", "", "cohortSize", "cohort_size", FALSE, "", "CohortSizeMax", "", "cohortSizeList", "cohort_sizes", FALSE, "", "CohortSizeMin", "", "cohortSizeList", "cohort_sizes", FALSE, "", "SafetyWindowSize", "", "patientGap", "gap", FALSE, "", "SafetyWindowSize", "", "sizeIntervals", "size", FALSE, "", "SafetyWindowSize", "", "patientFollow", "follow", FALSE, "", "SafetyWindowSize", "", "patientFollowMin", "follow_min", FALSE, "", "SafetyWindowConst", "", "patientGap", "gap", FALSE, "", "SafetyWindowConst", "", "patientFollow", "follow", FALSE, "", "SafetyWindowConst", "", "patientFollowMin", "follow_min", FALSE, "", "OneParExpNormalPrior", "OneParLogNormalPrior", "", "", FALSE, "", "Design", "", "PLcohortSize", "pl_cohort_size", FALSE, "", "TDsamplesDesign", "", "PLcohortSize", "pl_cohort_size", FALSE, "", "TDDesign", "", "PLcohortSize", "pl_cohort_size", FALSE, "" )
cc <- class_changes cc <- cc[order(cc$class_name, cc$class_new_name, cc$slot_name, cc$slot_new_name), ] no_slot_change <- nchar(trimws(cc$slot_name)) == 0 cc$class_name <- cell_spec( cc$class_name, strikeout = ifelse(cc$was_removed & no_slot_change, TRUE, FALSE) ) cc$slot_name <- cell_spec( cc$slot_name, strikeout = ifelse(cc$was_removed & !no_slot_change, TRUE, FALSE) ) cc <- cc[, -5] kbl(cc, longtable = TRUE, booktabs = TRUE, col.names = NULL, escape = FALSE) %>% add_header_above(c("Name", "New Name", "Name", "New Name", "")) %>% add_header_above(c("Class/Constructor[note]" = 2, "Slot/Arg[note]" = 2, "Comment")) %>% column_spec(1:5, width = "6cm", border_left = TRUE, border_right = TRUE) %>% collapse_rows(columns = 1, valign = "top") %>% kable_styling(latex_options = c("repeat_header"), font_size = 11) %>% add_footnote( c( "Class or class' user constructor. In the later case the Name/New Name is followed by ().", "Slot in case of the class or argument in case of the class' user constructor" ), notation = "symbol" )
Strikeout indicates that the class/slot was removed.
Methods
method_changes <- tribble( ~method_name, ~method_new_name, ~arg_name, ~arg_new_name, ~was_removed, ~output_changes, ~comment, "update-Data", "", "newCohort", "new_cohort", FALSE, "", "", "update-Data", "", "", "check", FALSE, "", "", "update-DataParts", "", "", "check", FALSE, "", "", "update-DataDual", "", "", "check", FALSE, "", "", "update-DataDA", "", "", "check", FALSE, "", "", "getEff", "", "", "", FALSE, "renamed elements in the list returned", "", "mcmc", "", "fromPrior", "from_prior", FALSE, "", "", "mcmc", "", "program", "", TRUE, "", "", "mcmc", "", "verbose", "", TRUE, "", "", "", "doseFunction", "", "", FALSE, "", "returns dose computing function for a given model", "", "probFunction", "", "", FALSE, "", "returns probability computing function for a given model", "dose", "", "prob", "x", FALSE, "", "", "", "efficacy", "", "", FALSE, "", "new generic function", "biomLevel", "biomarker", "", "", FALSE, "", "changed generic and methods", "biomLevel", "", "dose", "", TRUE, "", "changed generic and methods", "biomLevel-DualEndpoint", "", "dose", "", TRUE, "", "", "biomLevel", "", "", "xLevel", FALSE, "", "added only for generic, reordered arguments for generic and methods", "gain", "", "several arguments", "several arguments", FALSE, "", "several arguments (generic and methods) changed, see man page", "", "update-ModelPseudo", "", "", FALSE, "", "", "update-LogisticIndepBeta", "", "", "", TRUE, "", "replaced by `update-ModelPseudo`", "update-Effloglog", "", "", "", TRUE, "", "replaced by `update-ModelPseudo`", "update-EffFlexi", "", "", "", TRUE, "", "replaced by `update-ModelPseudo`", "nextBest-NextBestTD", "", "Effmodel", "model_eff", FALSE, "", "", "nextBest-NextBestTD", "", "Effsamples", "samples_eff", FALSE, "", "", "nextBest-NextBestTD", "", "SIM", "in_sim", FALSE, "", "", "nextBest-NextBestTDsamples", "", "Effmodel", "model_eff", FALSE, "", "", "nextBest-NextBestTDsamples", "", "Effsamples", "samples_eff", FALSE, "", "", "nextBest-NextBestMaxGain", "", "Effmodel", "model_eff", FALSE, "", "", "nextBest-NextBestMaxGain", "", "Effsamples", "samples_eff", FALSE, "", "", "nextBest-NextBestMaxGain", "", "SIM", "in_sim", FALSE, "", "", "nextBest-NextBestMaxGain", "", "", "", FALSE, "renamed elements in the list returned", "", "nextBest-NextBestMaxGainSamples", "", "Effmodel", "model_eff", FALSE, "", "", "nextBest-NextBestMaxGainSamples", "", "Effsamples", "samples_eff", FALSE, "", "", "nextBest-NextBestMaxGainSamples", "", "SIM", "in_sim", FALSE, "", "", "nextBest-NextBestMaxGainSamples", "", "", "", FALSE, "renamed elements in the list returned", "", "size", "size", "", "check", FALSE, "", "" )
mc <- method_changes mc <- mc[order(mc$method_name, mc$method_new_name, mc$arg_name, mc$arg_new_name), ] no_arg_change <- nchar(trimws(mc$arg_name)) == 0 mc$method_name <- cell_spec( mc$method_name, strikeout = ifelse(mc$was_removed & no_arg_change, TRUE, FALSE) ) mc$arg_name <- cell_spec( mc$arg_name, strikeout = ifelse(mc$was_removed & !no_arg_change, TRUE, FALSE) ) mc <- mc[, -5] kbl(mc, longtable = TRUE, booktabs = TRUE, col.names = NULL, escape = FALSE) %>% add_header_above(c("Name", "New Name", "Name", "New Name", "", "")) %>% add_header_above(c("Method" = 2, "Argument" = 2, "Output Changes", "Comment")) %>% column_spec(1:6, width = "6cm", border_left = TRUE, border_right = TRUE) %>% collapse_rows(columns = 1, valign = "top") %>% kable_styling(latex_options = c("repeat_header"), font_size = 11)
Strikeout indicates that the method/argument was removed.
References
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