minor new release

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: crmPack
 Title: Object-Oriented Implementation of Dose Escalation Designs
-Version: 2.0.1
+Version: 2.0.2
 Authors@R: c(
     person("Daniel", "Sabanes Bove", , "[email protected]", role = c("aut", "cre")),
     person("Wai", "Yin Yeung", , "[email protected]", role = "aut"),

NEWS.md

@@ -1,3 +1,9 @@
+# Version 2.0.2
+
+### Miscellaneous
+
+* Fixed logging tests which had been broken by recent `futile.logger` updates.
+
 # Version 2.0.1
 
 ### Bugfixes

design/check-timings.R

@@ -0,0 +1,10 @@
+# Run R CMD check (also devtools::check will include the --timings flag),
+# then read the timings file and show the top 10 longest running examples:
+
+timings <- read.table("../crmPack.Rcheck/crmPack-Ex.timings", header = TRUE)
+
+library(dplyr)
+
+timings |>
+  arrange(-elapsed) |>
+  head(10)

examples/Design-class-DADesign.R

@@ -57,7 +57,7 @@ my_stopping1 <- StoppingTargetProb(
 
 my_stopping2 <- StoppingMinPatients(nPatients = 50)
 
-my_stopping <- (my_stopping1 | my_stopping2)
+my_stopping <- (my_stopping1 | my_stopping2) | StoppingMissingDose()
 
 my_safety_window <- SafetyWindowConst(c(6, 2), 7, 7)
 

examples/Design-class-Design.R

@@ -32,7 +32,9 @@ my_stopping2 <- StoppingTargetProb(
   prob = 0.5
 )
 my_stopping3 <- StoppingMinPatients(nPatients = 20)
-my_stopping <- (my_stopping1 & my_stopping2) | my_stopping3
+my_stopping <- (my_stopping1 & my_stopping2) |
+  my_stopping3 |
+  StoppingMissingDose()
 
 # Choose the rule for dose increments.
 my_increments <- IncrementsRelative(

examples/Design-class-DesignGrouped.R

@@ -32,7 +32,9 @@ my_stopping2 <- StoppingTargetProb(
   prob = 0.5
 )
 my_stopping3 <- StoppingMinPatients(nPatients = 20)
-my_stopping <- (my_stopping1 & my_stopping2) | my_stopping3
+my_stopping <- (my_stopping1 & my_stopping2) |
+  my_stopping3 |
+  StoppingMissingDose()
 
 # Choose the rule for dose increments.
 my_increments <- IncrementsRelative(

examples/Design-class-DesignOrdinal.R

@@ -14,7 +14,10 @@ my_stopping2 <- StoppingTargetProb(
   prob = 0.5
 )
 my_stopping3 <- StoppingMinPatients(nPatients = 20)
-my_stopping <- StoppingOrdinal(1L, (my_stopping1 & my_stopping2) | my_stopping3)
+my_stopping <- StoppingOrdinal(
+  1L,
+  (my_stopping1 & my_stopping2) | my_stopping3 | StoppingMissingDose()
+)
 
 # Initialize the design.
 design <- DesignOrdinal(

examples/Design-class-DualDesign.R

@@ -33,7 +33,7 @@ my_stopping1 <- StoppingTargetBiomarker(
   target = c(0.9, 1),
   prob = 0.5
 )
-my_stopping <- my_stopping1 | StoppingMinPatients(40)
+my_stopping <- my_stopping1 | StoppingMinPatients(40) | StoppingMissingDose()
 
 # Choose the rule for dose increments.
 my_increments <- IncrementsRelative(

examples/Design-class-DualResponsesDesign.R

@@ -25,7 +25,7 @@ my_increments <- IncrementsRelative(
 )
 
 my_size <- CohortSizeConst(size = 3)
-my_stopping <- StoppingMinPatients(nPatients = 36)
+my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()
 
 design <- DualResponsesDesign(
   nextBest = my_next_best,

examples/Design-class-DualResponsesSamplesDesign.R

@@ -33,7 +33,7 @@ my_increments <- IncrementsRelative(
   increments = c(2, 2)
 )
 my_size <- CohortSizeConst(size = 3)
-my_stopping <- StoppingMinPatients(nPatients = 36)
+my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()
 
 design <- DualResponsesSamplesDesign(
   nextBest = my_next_best,

examples/Design-class-TDDesign.R

@@ -24,7 +24,7 @@ my_increments <- IncrementsRelative(
 )
 
 # Stop when the maximum sample size of 36 patients is reached.
-my_stopping <- StoppingMinPatients(nPatients = 36)
+my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()
 
 # The design with all the above information and starting with a dose of 25.
 # This design incorporates only DLT responses and no DLT samples are involved

examples/Design-class-TDsamplesDesign.R

@@ -27,7 +27,7 @@ my_increments <- IncrementsRelative(
 )
 
 # Stop when the maximum sample size of 36 patients is reached.
-my_stopping <- StoppingMinPatients(nPatients = 36)
+my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()
 
 # The design with all the above information and starting with a dose of 25.
 design <- TDsamplesDesign(

examples/Design-method-simulate-DesignGrouped.R

@@ -52,6 +52,7 @@ legend("topright", c("mono", "combo"), lty = c(1, 2), col = c(1, 2))
 
 # Start the simulations.
 set.seed(123)
+\donttest{
 my_sims <- simulate(
   my_design,
   nsim = 1, # This should be at least 100 in actual applications.
@@ -74,3 +75,4 @@ plot(combo_sims_sum)
 trial_index <- 1
 plot(my_sims$mono@data[[trial_index]])
 plot(my_sims$combo@data[[trial_index]])
+}

examples/Rules-method-nextBest-NextBestDualEndpoint.R

@@ -57,7 +57,7 @@ my_model <- DualEndpointRW(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 50)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 
 \donttest{
 my_samples <- mcmc(my_data, my_model, my_options)

examples/Rules-method-nextBest-NextBestEWOC.R

@@ -17,7 +17,7 @@ my_model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 50)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.

examples/Rules-method-nextBest-NextBestMTD.R

@@ -17,7 +17,7 @@ my_model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 50)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.

examples/Rules-method-nextBest-NextBestMaxGainSamples.R

@@ -28,7 +28,7 @@ my_model_effll <- Effloglog(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 50)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples_dlt <- mcmc(my_data, my_model_dlt, my_options)
 my_samples_effll <- mcmc(my_data, my_model_effll, my_options)
 

examples/Rules-method-nextBest-NextBestMinDist.R

@@ -22,7 +22,7 @@ my_model <- LogisticKadaneBetaGamma(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 50)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.

examples/Rules-method-nextBest-NextBestNCRM-DataParts.R

@@ -20,7 +20,7 @@ my_model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 50)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.

examples/Rules-method-nextBest-NextBestNCRM.R

@@ -17,7 +17,7 @@ my_model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 50)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.

examples/Rules-method-nextBest-NextBestNCRMLoss.R

@@ -17,7 +17,7 @@ my_model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 50)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.

examples/Rules-method-nextBest-NextBestProbMTDLTE.R

@@ -17,7 +17,7 @@ my_model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 50)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.

examples/Rules-method-nextBest-NextBestProbMtdMinDist.R

@@ -21,7 +21,7 @@ my_model <- my_model <- LogisticKadaneBetaGamma(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 50)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.

examples/Rules-method-stopTrial-StoppingAll.R

@@ -16,7 +16,7 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
 samples <- mcmc(data, model, options)
 

examples/Rules-method-stopTrial-StoppingAny.R

@@ -16,7 +16,7 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
 samples <- mcmc(data, model, options)
 

examples/Rules-method-stopTrial-StoppingCohortsNearDose.R

@@ -16,7 +16,7 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
 samples <- mcmc(data, model, options)
 

examples/Rules-method-stopTrial-StoppingHighestDose.R

@@ -16,7 +16,7 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
 samples <- mcmc(data, model, options)
 

examples/Rules-method-stopTrial-StoppingList.R

@@ -16,7 +16,7 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
 samples <- mcmc(data, model, options)
 

examples/Rules-method-stopTrial-StoppingLowestDoseHSRBeta.R

@@ -22,8 +22,9 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
+\donttest{
 samples <- mcmc(data, model, options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.
@@ -66,3 +67,4 @@ stopTrial(
   model = model,
   data = data
 )
+}

examples/Rules-method-stopTrial-StoppingMTDCV.R

@@ -14,7 +14,7 @@ my_model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.

examples/Rules-method-stopTrial-StoppingMTDdistribution.R

@@ -16,7 +16,7 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
 samples <- mcmc(data, model, options)
 

examples/Rules-method-stopTrial-StoppingMinCohorts.R

@@ -16,7 +16,7 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
 samples <- mcmc(data, model, options)
 

examples/Rules-method-stopTrial-StoppingMinPatients.R

@@ -16,7 +16,7 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
 samples <- mcmc(data, model, options)
 

examples/Rules-method-stopTrial-StoppingMissingDose.R

@@ -26,7 +26,7 @@ my_model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed.

examples/Rules-method-stopTrial-StoppingOrdinal.R

@@ -1,6 +1,6 @@
 data <- .DefaultDataOrdinal()
 model <- .DefaultLogisticLogNormalOrdinal()
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 samples <- mcmc(data, model, options)
 
 myIncrements <- .DefaultIncrementsOrdinal()

examples/Rules-method-stopTrial-StoppingPatientsNearDose.R

@@ -16,7 +16,7 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
 samples <- mcmc(data, model, options)
 

examples/Rules-method-stopTrial-StoppingSpecificDose.R

@@ -15,7 +15,7 @@ my_model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior.
-my_options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 my_samples <- mcmc(my_data, my_model, my_options)
 
 # Define the rule which will be used to select the next best dose

examples/Rules-method-stopTrial-StoppingTargetBiomarker.R

@@ -37,8 +37,9 @@ model <- DualEndpointRW(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
+\donttest{
 samples <- mcmc(data, model, options)
 
 # Define the rule for dose increments and calculate the maximum dose allowed
@@ -79,5 +80,5 @@ stopTrial(
   model = model,
   data = data
 )
-
+}
 # nolint end

examples/Rules-method-stopTrial-StoppingTargetProb.R

@@ -16,7 +16,7 @@ model <- LogisticLogNormal(
 )
 
 # Set-up some MCMC parameters and generate samples from the posterior
-options <- McmcOptions(burnin = 10, step = 2, samples = 40)
+options <- McmcOptions(burnin = 5, step = 1, samples = 10)
 set.seed(94)
 samples <- mcmc(data, model, options)