Running ‘testthat.R’ [121s/300s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(trialr)
  Loading required package: Rcpp
  > 
  > test_check("trialr")
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 0 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 1: Adjust your expectations accordingly!
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  Chain 1: 
  Chain 1:  Elapsed Time: 0.020798 seconds (Warm-up)
  Chain 1:                0.017874 seconds (Sampling)
  Chain 1:                0.038672 seconds (Total)
  Chain 1: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 0 seconds
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  Chain 2: Adjust your expectations accordingly!
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  Chain 2:  Elapsed Time: 0.020798 seconds (Warm-up)
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  Chain 2:                0.042565 seconds (Total)
  Chain 2: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 0 seconds
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  Chain 3: Adjust your expectations accordingly!
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  Chain 3: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 0 seconds
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  Chain 4: Adjust your expectations accordingly!
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  Chain 4:  Elapsed Time: 0.024285 seconds (Warm-up)
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  Chain 4:                0.045215 seconds (Total)
  Chain 4: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 0 seconds
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  Chain 1: Adjust your expectations accordingly!
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  Chain 1: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 0 seconds
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  Chain 2: 
  Chain 2:  Elapsed Time: 0.022786 seconds (Warm-up)
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  Chain 2:                0.045553 seconds (Total)
  Chain 2: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 0 seconds
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  Chain 3: Adjust your expectations accordingly!
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  Chain 3:  Elapsed Time: 0.025657 seconds (Warm-up)
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  Chain 3:                0.051363 seconds (Total)
  Chain 3: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 0 seconds
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  Chain 4: Adjust your expectations accordingly!
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  Chain 4: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 0 seconds
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  Chain 1: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 2).
  Chain 2: 
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  Chain 2: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 3).
  Chain 3: 
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  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 4).
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  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 1).
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  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 2).
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  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 3).
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  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 4).
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  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 3).
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  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 4).
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  Chain 2: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 0 seconds
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  Chain 3: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 0 seconds
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  Chain 4: Adjust your expectations accordingly!
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  Chain 4: 
  ── 1. Error: careful_escalation advocates start_dose when n = 0 (@test_stan_crm.
  Assigned data `draws[, c(".chain", ".iteration", ".draw")]` must be compatible with existing data.
  ✖ Existing data has 16000 rows.
  ✖ Element 1 of assigned data has 4000 rows.
  ℹ Only vectors of size 1 are recycled.
  Backtrace:
    1. trialr::careful_escalation(...)
    3. trialr:::prob_tox_exceeds.dose_finding_fit(...)
   10. tidybayes::gather_draws(., prob_tox[dose])
   12. base::lapply(...)
   13. tidybayes:::FUN(X[[i]], ...)
   16. tidybayes:::spread_draws_(...)
   17. tidybayes:::spread_draws_long_(...)
   19. tibble:::`[<-.tbl_df`(...)
   20. tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   21. tibble:::vectbl_recycle_rhs(...)
   22. base::tryCatch(...)
   23. base:::tryCatchList(expr, classes, parentenv, handlers)
   24. base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])
   25. value[[3L]](cond)
  
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 0 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 1: Adjust your expectations accordingly!
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  Chain 1: 
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  Chain 1:                0.053601 seconds (Total)
  Chain 1: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 0 seconds
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  Chain 2: Adjust your expectations accordingly!
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  Chain 2:                0.052596 seconds (Total)
  Chain 2: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 0 seconds
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  Chain 3: Adjust your expectations accordingly!
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  Chain 3: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 0 seconds
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  Chain 4: Adjust your expectations accordingly!
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  Chain 4:                0.052943 seconds (Total)
  Chain 4: 
  ── 2. Error: careful_escalation does not skip doses (@test_stan_crm.R#386)  ────
  Assigned data `draws[, c(".chain", ".iteration", ".draw")]` must be compatible with existing data.
  ✖ Existing data has 16000 rows.
  ✖ Element 1 of assigned data has 4000 rows.
  ℹ Only vectors of size 1 are recycled.
  Backtrace:
    1. trialr::careful_escalation(fit1, tox_threshold = 0.33, certainty_threshold = 0.8)
    3. trialr:::prob_tox_exceeds.dose_finding_fit(...)
   10. tidybayes::gather_draws(., prob_tox[dose])
   12. base::lapply(...)
   13. tidybayes:::FUN(X[[i]], ...)
   16. tidybayes:::spread_draws_(...)
   17. tidybayes:::spread_draws_long_(...)
   19. tibble:::`[<-.tbl_df`(...)
   20. tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   21. tibble:::vectbl_recycle_rhs(...)
   22. base::tryCatch(...)
   23. base:::tryCatchList(expr, classes, parentenv, handlers)
   24. base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])
   25. value[[3L]](cond)
  
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 0 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 1: Adjust your expectations accordingly!
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  Chain 1: 
  Chain 1:  Elapsed Time: 0.0308 seconds (Warm-up)
  Chain 1:                0.034814 seconds (Sampling)
  Chain 1:                0.065614 seconds (Total)
  Chain 1: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 0 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 2: Adjust your expectations accordingly!
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  Chain 2:                0.06653 seconds (Total)
  Chain 2: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 0 seconds
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  Chain 3: Adjust your expectations accordingly!
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  Chain 3:                0.067721 seconds (Total)
  Chain 3: 
  
  SAMPLING FOR MODEL 'CrmEmpiricNormalPrior' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 0 seconds
  Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 4: Adjust your expectations accordingly!
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  Chain 4: 
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  Chain 4:                0.062526 seconds (Total)
  Chain 4: 
  ── 3. Error: careful_escalation stops appropriately (@test_stan_crm.R#405)  ────
  Assigned data `draws[, c(".chain", ".iteration", ".draw")]` must be compatible with existing data.
  ✖ Existing data has 16000 rows.
  ✖ Element 1 of assigned data has 4000 rows.
  ℹ Only vectors of size 1 are recycled.
  Backtrace:
    1. trialr::careful_escalation(...)
    3. trialr:::prob_tox_exceeds.dose_finding_fit(...)
   10. tidybayes::gather_draws(., prob_tox[dose])
   12. base::lapply(...)
   13. tidybayes:::FUN(X[[i]], ...)
   16. tidybayes:::spread_draws_(...)
   17. tidybayes:::spread_draws_long_(...)
   19. tibble:::`[<-.tbl_df`(...)
   20. tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   21. tibble:::vectbl_recycle_rhs(...)
   22. base::tryCatch(...)
   23. base:::tryCatchList(expr, classes, parentenv, handlers)
   24. base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])
   25. value[[3L]](cond)
  
  
  SAMPLING FOR MODEL 'EffTox' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 0 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 1: Adjust your expectations accordingly!
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  Chain 1: 
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  Chain 1:                0.040725 seconds (Sampling)
  Chain 1:                0.146071 seconds (Total)
  Chain 1: 
  
  SAMPLING FOR MODEL 'EffTox' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 0 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 2: Adjust your expectations accordingly!
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  Chain 2:                0.043627 seconds (Sampling)
  Chain 2:                0.149606 seconds (Total)
  Chain 2: 
  
  SAMPLING FOR MODEL 'EffTox' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 0 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 1: Adjust your expectations accordingly!
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  Chain 1:                0.32082 seconds (Total)
  Chain 1: 
  
  SAMPLING FOR MODEL 'EffTox' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 0 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 2: Adjust your expectations accordingly!
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  Chain 2:                0.363642 seconds (Total)
  Chain 2: 
  
  SAMPLING FOR MODEL 'EffTox' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 0 seconds
  Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 3: Adjust your expectations accordingly!
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  Chain 3:                0.231814 seconds (Total)
  Chain 3: 
  
  SAMPLING FOR MODEL 'EffTox' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 0 seconds
  Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Chain 4: Adjust your expectations accordingly!
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  Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
  Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
  Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
  Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
  Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
  Chain 4: 
  Chain 4:  Elapsed Time: 0.240476 seconds (Warm-up)
  Chain 4:                0.173703 seconds (Sampling)
  Chain 4:                0.414179 seconds (Total)
  Chain 4: 
  ══ testthat results  ═══════════════════════════════════════════════════════════
  [ OK: 732 | SKIPPED: 0 | WARNINGS: 37 | FAILED: 3 ]
  1. Error: careful_escalation advocates start_dose when n = 0 (@test_stan_crm.R#361) 
  2. Error: careful_escalation does not skip doses (@test_stan_crm.R#386) 
  3. Error: careful_escalation stops appropriately (@test_stan_crm.R#405) 
  
  Error: testthat unit tests failed
  Execution halted