Running ‘testthat.R’ [54s/79s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > 
  > library(xgboost)
  > library(randomForest)
  randomForest 4.7-1.1
  Type rfNews() to see new features/changes/bug fixes.
  > library(kernlab)
  > library(earth)
  Loading required package: Formula
  Loading required package: plotmo
  Loading required package: plotrix
  Loading required package: TeachingDemos
  > library(thief)
  Loading required package: forecast
  > library(smooth)
  Loading required package: greybox
  Package "greybox", v1.0.7 loaded.
  Did you know that you can use your own loss function in alm()? This is regulated with 'loss' parameter. See documentation for examples.
  
  This is package "smooth", v3.2.0
  
  > library(greybox)
  > 
  > library(stats)
  > 
  > library(tidymodels)
  ── Attaching packages ────────────────────────────────────── tidymodels 1.0.0 ──
  ✔ broom        1.0.4     ✔ recipes      1.0.5
  ✔ dials        1.1.0     ✔ rsample      1.1.1
  ✔ dplyr        1.1.0     ✔ tibble       3.2.0
  ✔ ggplot2      3.4.1     ✔ tidyr        1.3.0
  ✔ infer        1.0.4     ✔ tune         1.0.1
  ✔ modeldata    1.1.0     ✔ workflows    1.1.3
  ✔ parsnip      1.0.4     ✔ workflowsets 1.0.0
  ✔ purrr        1.0.1     ✔ yardstick    1.1.0
  ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
  ✖ yardstick::accuracy() masks forecast::accuracy()
  ✖ ggplot2::alpha()      masks scales::alpha(), kernlab::alpha()
  ✖ dplyr::combine()      masks randomForest::combine()
  ✖ purrr::cross()        masks kernlab::cross()
  ✖ purrr::discard()      masks scales::discard()
  ✖ dplyr::filter()       masks stats::filter()
  ✖ purrr::is_null()      masks testthat::is_null()
  ✖ dplyr::lag()          masks stats::lag()
  ✖ ggplot2::margin()     masks randomForest::margin()
  ✖ tidyr::matches()      masks rsample::matches(), dplyr::matches(), testthat::matches()
  ✖ parsnip::pls()        masks smooth::pls()
  ✖ dplyr::slice()        masks xgboost::slice()
  ✖ tidyr::spread()       masks greybox::spread()
  ✖ recipes::step()       masks stats::step()
  • Dig deeper into tidy modeling with R at https://www.tmwr.org
  > library(parsnip)
  > library(workflows)
  > library(rsample)
  > library(recipes)
  > library(tune)
  > library(dials)
  > library(TSrepr)
  
  Attaching package: 'TSrepr'
  
  The following objects are masked from 'package:yardstick':
  
      mae, mape, mase, rmse, smape
  
  > library(yardstick)
  > library(slider)
  > 
  > library(timetk)
  > library(modeltime)
  
  Attaching package: 'modeltime'
  
  The following object is masked from 'package:TSrepr':
  
      maape
  
  The following object is masked from 'package:kernlab':
  
      error
  
  > 
  > library(tidyverse)
  ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
  ✔ forcats   1.0.0     ✔ readr     2.1.4
  ✔ lubridate 1.9.2     ✔ stringr   1.5.0
  ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
  ✖ ggplot2::alpha()       masks scales::alpha(), kernlab::alpha()
  ✖ readr::col_factor()    masks scales::col_factor()
  ✖ dplyr::combine()       masks randomForest::combine()
  ✖ purrr::cross()         masks kernlab::cross()
  ✖ purrr::discard()       masks scales::discard()
  ✖ readr::edition_get()   masks testthat::edition_get()
  ✖ dplyr::filter()        masks stats::filter()
  ✖ stringr::fixed()       masks recipes::fixed()
  ✖ lubridate::hm()        masks greybox::hm()
  ✖ purrr::is_null()       masks testthat::is_null()
  ✖ dplyr::lag()           masks stats::lag()
  ✖ readr::local_edition() masks testthat::local_edition()
  ✖ ggplot2::margin()      masks randomForest::margin()
  ✖ tidyr::matches()       masks rsample::matches(), dplyr::matches(), testthat::matches()
  ✖ dplyr::slice()         masks xgboost::slice()
  ✖ readr::spec()          masks yardstick::spec()
  ✖ tidyr::spread()        masks greybox::spread()
  ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
  > library(lubridate)
  > 
  > 
  > test_check("modeltime")
  
  
  
  
  
  
  
  
  
  [ FAIL 26 | WARN 2 | SKIP 20 | PASS 50 ]
  
  ══ Skipped tests ═══════════════════════════════════════════════════════════════
  • On CRAN (20)
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-algo-arima_boost-Arima.R:32:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% set_engine("arima_xgboost") at test-algo-arima_boost-Arima.R:32:0
    2. ├─parsnip::set_engine(., "arima_xgboost")
    3. └─modeltime::arima_boost(...)
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-arima_boost-auto_arima.R:14:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% set_engine("auto_arima_xgboost") at test-algo-arima_boost-auto_arima.R:14:0
    2. ├─parsnip::set_engine(., "auto_arima_xgboost")
    3. └─modeltime::arima_boost(...)
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-arima_reg-Arima.R:14:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% set_engine("arima") at test-algo-arima_reg-Arima.R:14:0
    2. ├─parsnip::set_engine(., "arima")
    3. └─modeltime::arima_reg(...)
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-arima_reg-auto_arima.R:18:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─arima_reg(seasonal_period = 12) %>% set_engine("auto_arima") at test-algo-arima_reg-auto_arima.R:18:0
    2. ├─parsnip::set_engine(., "auto_arima")
    3. └─modeltime::arima_reg(seasonal_period = 12)
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-exp_smoothing-ets.R:14:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─exp_smoothing() %>% set_engine("ets") at test-algo-exp_smoothing-ets.R:14:0
    2. ├─parsnip::set_engine(., "ets")
    3. └─modeltime::exp_smoothing()
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-prophet_boost.R:14:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% set_engine("prophet_xgboost") at test-algo-prophet_boost.R:14:0
    2. ├─parsnip::set_engine(., "prophet_xgboost")
    3. └─modeltime::prophet_boost(...)
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-prophet_reg.R:14:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% set_engine("prophet") at test-algo-prophet_reg.R:14:0
    2. ├─parsnip::set_engine(., "prophet")
    3. └─modeltime::prophet_reg(...)
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-seasonal_decomp_ets.R:18:5'): seasonal_reg: stlm_ets ──────
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% set_engine("stlm_ets") at test-algo-seasonal_decomp_ets.R:18:4
    2. ├─parsnip::set_engine(., "stlm_ets")
    3. └─modeltime::seasonal_reg(seasonal_period_1 = "1 day", seasonal_period_2 = "week")
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-seasonal_reg_tbats.R:14:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─seasonal_reg() %>% set_engine("tbats") at test-algo-seasonal_reg_tbats.R:14:0
    2. ├─parsnip::set_engine(., "tbats")
    3. └─modeltime::seasonal_reg()
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-window_reg.R:24:5'): NAIVE - Single Time Series (No ID) ───
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─naive_reg() %>% set_engine("naive") %>% ... at test-algo-window_reg.R:24:4
    2. ├─generics::fit(., value ~ date, data = training(splits))
    3. ├─parsnip::set_engine(., "naive")
    4. └─modeltime::naive_reg()
    5.   └─parsnip::new_model_spec(...)
    6.     └─parsnip::spec_is_possible(spec = out)
    7.       ├─dplyr::full_join(...)
    8.       └─dplyr:::full_join.data.frame(...)
    9.         └─dplyr:::join_mutate(...)
   10.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   12.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   13.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14.                 ├─base::withCallingHandlers(...)
   15.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-window_reg.R:67:5'): NAIVE - Multiple Time Series (Panel uses ID) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─naive_reg(id = "id") %>% set_engine("naive") %>% ... at test-algo-window_reg.R:67:4
    2. ├─generics::fit(., value ~ date + id, data = data_prepared_tbl)
    3. ├─parsnip::set_engine(., "naive")
    4. └─modeltime::naive_reg(id = "id")
    5.   └─parsnip::new_model_spec(...)
    6.     └─parsnip::spec_is_possible(spec = out)
    7.       ├─dplyr::full_join(...)
    8.       └─dplyr:::full_join.data.frame(...)
    9.         └─dplyr:::join_mutate(...)
   10.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   12.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   13.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14.                 ├─base::withCallingHandlers(...)
   15.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-window_reg.R:96:5'): NAIVE - Check New Factors ────────────
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% fit(data_prepared_tbl) at test-algo-window_reg.R:96:4
    2. ├─generics::fit(., data_prepared_tbl)
    3. ├─workflows::add_recipe(., recipe(value ~ date + id, data = data_prepared_tbl))
    4. │ └─workflows:::add_action(x, action, "recipe")
    5. │   └─workflows:::validate_is_workflow(x, call = call)
    6. │     └─workflows:::is_workflow(x)
    7. ├─workflows::add_model(., naive_reg(id = "id") %>% set_engine("naive"))
    8. │ └─workflows:::new_action_model(spec, formula)
    9. │   └─workflows:::is_model_spec(spec)
   10. ├─naive_reg(id = "id") %>% set_engine("naive")
   11. ├─parsnip::set_engine(., "naive")
   12. └─modeltime::naive_reg(id = "id")
   13.   └─parsnip::new_model_spec(...)
   14.     └─parsnip::spec_is_possible(spec = out)
   15.       ├─dplyr::full_join(...)
   16.       └─dplyr:::full_join.data.frame(...)
   17.         └─dplyr:::join_mutate(...)
   18.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   19.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   20.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   21.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   22.                 ├─base::withCallingHandlers(...)
   23.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-window_reg.R:147:5'): SNAIVE - Single Time Series (No ID) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─naive_reg() %>% set_engine("snaive") %>% ... at test-algo-window_reg.R:147:4
    2. ├─generics::fit(., value ~ date, data = training(splits))
    3. ├─parsnip::set_engine(., "snaive")
    4. └─modeltime::naive_reg()
    5.   └─parsnip::new_model_spec(...)
    6.     └─parsnip::spec_is_possible(spec = out)
    7.       ├─dplyr::full_join(...)
    8.       └─dplyr:::full_join.data.frame(...)
    9.         └─dplyr:::join_mutate(...)
   10.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   12.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   13.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14.                 ├─base::withCallingHandlers(...)
   15.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-window_reg.R:198:5'): SNAIVE - Multiple Time Series (Panel ID) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─naive_reg(id = "id") %>% set_engine("snaive") %>% ... at test-algo-window_reg.R:198:4
    2. ├─generics::fit(., value ~ date + id, data = data_prepared_tbl)
    3. ├─parsnip::set_engine(., "snaive")
    4. └─modeltime::naive_reg(id = "id")
    5.   └─parsnip::new_model_spec(...)
    6.     └─parsnip::spec_is_possible(spec = out)
    7.       ├─dplyr::full_join(...)
    8.       └─dplyr:::full_join.data.frame(...)
    9.         └─dplyr:::join_mutate(...)
   10.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   12.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   13.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14.                 ├─base::withCallingHandlers(...)
   15.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-window_reg.R:233:5'): SNAIVE - Check New Factors ──────────
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% fit(data_prepared_tbl) at test-algo-window_reg.R:233:4
    2. ├─generics::fit(., data_prepared_tbl)
    3. ├─workflows::add_recipe(., recipe(value ~ date + id, data = data_prepared_tbl))
    4. │ └─workflows:::add_action(x, action, "recipe")
    5. │   └─workflows:::validate_is_workflow(x, call = call)
    6. │     └─workflows:::is_workflow(x)
    7. ├─workflows::add_model(., naive_reg(id = "id") %>% set_engine("snaive"))
    8. │ └─workflows:::new_action_model(spec, formula)
    9. │   └─workflows:::is_model_spec(spec)
   10. ├─naive_reg(id = "id") %>% set_engine("snaive")
   11. ├─parsnip::set_engine(., "snaive")
   12. └─modeltime::naive_reg(id = "id")
   13.   └─parsnip::new_model_spec(...)
   14.     └─parsnip::spec_is_possible(spec = out)
   15.       ├─dplyr::full_join(...)
   16.       └─dplyr:::full_join.data.frame(...)
   17.         └─dplyr:::join_mutate(...)
   18.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   19.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   20.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   21.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   22.                 ├─base::withCallingHandlers(...)
   23.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-window_reg.R:284:5'): WINDOW - Single Time Series (No ID) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% fit(value ~ date, data = training(splits)) at test-algo-window_reg.R:284:4
    2. ├─generics::fit(., value ~ date, data = training(splits))
    3. ├─parsnip::set_engine(...)
    4. └─modeltime::window_reg(window_size = 24)
    5.   └─parsnip::new_model_spec(...)
    6.     └─parsnip::spec_is_possible(spec = out)
    7.       ├─dplyr::full_join(...)
    8.       └─dplyr:::full_join.data.frame(...)
    9.         └─dplyr:::join_mutate(...)
   10.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   12.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   13.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14.                 ├─base::withCallingHandlers(...)
   15.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-window_reg.R:352:5'): WINDOW - Multiple Time Series (Panel ID) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% fit(value ~ date + id, data = data_prepared_tbl) at test-algo-window_reg.R:352:4
    2. ├─generics::fit(., value ~ date + id, data = data_prepared_tbl)
    3. ├─parsnip::set_engine(., "window_function", window_function = mean)
    4. └─modeltime::window_reg(id = "id", window_size = 12)
    5.   └─parsnip::new_model_spec(...)
    6.     └─parsnip::spec_is_possible(spec = out)
    7.       ├─dplyr::full_join(...)
    8.       └─dplyr:::full_join.data.frame(...)
    9.         └─dplyr:::join_mutate(...)
   10.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   12.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   13.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14.                 ├─base::withCallingHandlers(...)
   15.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-algo-window_reg.R:390:5'): SNAIVE - Check New Factors ──────────
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% fit(data_prepared_tbl) at test-algo-window_reg.R:390:4
    2. ├─generics::fit(., data_prepared_tbl)
    3. ├─workflows::add_recipe(., recipe(value ~ date + id, data = data_prepared_tbl))
    4. │ └─workflows:::add_action(x, action, "recipe")
    5. │   └─workflows:::validate_is_workflow(x, call = call)
    6. │     └─workflows:::is_workflow(x)
    7. ├─workflows::add_model(., window_reg(id = "id") %>% set_engine("window_function"))
    8. │ └─workflows:::new_action_model(spec, formula)
    9. │   └─workflows:::is_model_spec(spec)
   10. ├─window_reg(id = "id") %>% set_engine("window_function")
   11. ├─parsnip::set_engine(., "window_function")
   12. └─modeltime::window_reg(id = "id")
   13.   └─parsnip::new_model_spec(...)
   14.     └─parsnip::spec_is_possible(spec = out)
   15.       ├─dplyr::full_join(...)
   16.       └─dplyr:::full_join.data.frame(...)
   17.         └─dplyr:::join_mutate(...)
   18.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   19.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   20.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   21.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   22.                 ├─base::withCallingHandlers(...)
   23.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-helpers-combine-modeltime-tables.R:17:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─arima_reg() %>% set_engine("auto_arima") %>% ... at test-helpers-combine-modeltime-tables.R:17:0
    2. ├─generics::fit(., value ~ date, training(splits))
    3. ├─parsnip::set_engine(., "auto_arima")
    4. └─modeltime::arima_reg()
    5.   └─parsnip::new_model_spec(...)
    6.     └─parsnip::spec_is_possible(spec = out)
    7.       ├─dplyr::full_join(...)
    8.       └─dplyr:::full_join.data.frame(...)
    9.         └─dplyr:::join_mutate(...)
   10.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   12.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   13.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14.                 ├─base::withCallingHandlers(...)
   15.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-helpers-pull_parsnip_preprocessor.R:13:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─arima_reg() %>% set_engine(engine = "auto_arima") %>% ... at test-helpers-pull_parsnip_preprocessor.R:13:0
    2. ├─generics::fit(., form, data = training(splits))
    3. ├─parsnip::set_engine(., engine = "auto_arima")
    4. └─modeltime::arima_reg()
    5.   └─parsnip::new_model_spec(...)
    6.     └─parsnip::spec_is_possible(spec = out)
    7.       ├─dplyr::full_join(...)
    8.       └─dplyr:::full_join.data.frame(...)
    9.         └─dplyr:::join_mutate(...)
   10.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   12.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   13.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14.                 ├─base::withCallingHandlers(...)
   15.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-helpers-update-modeltime-tables.R:12:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─arima_reg(seasonal_period = 1) %>% set_engine("auto_arima") at test-helpers-update-modeltime-tables.R:12:0
    2. ├─parsnip::set_engine(., "auto_arima")
    3. └─modeltime::arima_reg(seasonal_period = 1)
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-modeltime_table-forecast-accuracy-refitting.R:18:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─arima_reg() %>% set_engine(engine = "auto_arima") %>% ... at test-modeltime_table-forecast-accuracy-refitting.R:18:0
    2. ├─generics::fit(., log(value) ~ date, data = training(splits))
    3. ├─parsnip::set_engine(., engine = "auto_arima")
    4. └─modeltime::arima_reg()
    5.   └─parsnip::new_model_spec(...)
    6.     └─parsnip::spec_is_possible(spec = out)
    7.       ├─dplyr::full_join(...)
    8.       └─dplyr:::full_join.data.frame(...)
    9.         └─dplyr:::join_mutate(...)
   10.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   12.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   13.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14.                 ├─base::withCallingHandlers(...)
   15.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-nested-modeltime.R:145:5'): modeltime_nested_fit: Good + Bad Model ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─testthat::expect_warning(...) at test-nested-modeltime.R:145:4
    2. │ └─testthat:::quasi_capture(...)
    3. │   ├─testthat (local) .capture(...)
    4. │   │ └─base::withCallingHandlers(...)
    5. │   └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. ├─nested_modeltime_tbl %>% ...
    7. ├─modeltime::modeltime_nested_select_best(., metric = "rsq", minimize = FALSE)
    8. │ └─... %>% ...
    9. ├─dplyr::mutate(...)
   10. ├─tidyr::nest(., .modeltime_tables = -(!!id_expr))
   11. ├─dplyr::right_join(., best_model_by_id_tbl, by = c(id_text, ".model_id"))
   12. └─dplyr:::right_join.data.frame(...)
   13.   └─dplyr:::join_mutate(...)
   14.     ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,2]>`)
   15.     └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,2]>`)
   16.       └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   17.         └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   18.           ├─base::withCallingHandlers(...)
   19.           └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-panel-data.R:16:1'): (code run outside of `test_that()`) ───────
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─... %>% fit(data_set) at test-panel-data.R:16:0
    2. ├─generics::fit(., data_set)
    3. ├─workflows::add_recipe(., recipe_spec)
    4. │ └─workflows:::add_action(x, action, "recipe")
    5. │   └─workflows:::validate_is_workflow(x, call = call)
    6. │     └─workflows:::is_workflow(x)
    7. ├─workflows::add_model(...)
    8. │ └─workflows:::new_action_model(spec, formula)
    9. │   └─workflows:::is_model_spec(spec)
   10. ├─... %>% set_engine("prophet_xgboost")
   11. ├─parsnip::set_engine(., "prophet_xgboost")
   12. └─modeltime::prophet_boost(...)
   13.   └─parsnip::new_model_spec(...)
   14.     └─parsnip::spec_is_possible(spec = out)
   15.       ├─dplyr::full_join(...)
   16.       └─dplyr:::full_join.data.frame(...)
   17.         └─dplyr:::join_mutate(...)
   18.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   19.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   20.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   21.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   22.                 ├─base::withCallingHandlers(...)
   23.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-results-accuracy-tables.R:11:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─arima_reg() %>% set_engine("auto_arima") %>% ... at test-results-accuracy-tables.R:11:0
    2. ├─generics::fit(., value ~ date, training(splits))
    3. ├─parsnip::set_engine(., "auto_arima")
    4. └─modeltime::arima_reg()
    5.   └─parsnip::new_model_spec(...)
    6.     └─parsnip::spec_is_possible(spec = out)
    7.       ├─dplyr::full_join(...)
    8.       └─dplyr:::full_join.data.frame(...)
    9.         └─dplyr:::join_mutate(...)
   10.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   12.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   13.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14.                 ├─base::withCallingHandlers(...)
   15.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ── Error ('test-results-forecast-plots.R:11:1'): (code run outside of `test_that()`) ──
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─arima_reg(seasonal_period = 12) %>% set_engine("auto_arima") at test-results-forecast-plots.R:11:0
    2. ├─parsnip::set_engine(., "auto_arima")
    3. └─modeltime::arima_reg(seasonal_period = 12)
    4.   └─parsnip::new_model_spec(...)
    5.     └─parsnip::spec_is_possible(spec = out)
    6.       ├─dplyr::full_join(...)
    7.       └─dplyr:::full_join.data.frame(...)
    8.         └─dplyr:::join_mutate(...)
    9.           ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   10.           └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,3]>`)
   11.             └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12.               └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   13.                 ├─base::withCallingHandlers(...)
   14.                 └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  
  [ FAIL 26 | WARN 2 | SKIP 20 | PASS 50 ]
  Error: Test failures
  Execution halted