* using log directory ‘/Users/ripley/R/packages/tests-devel/tidyrules.Rcheck’
* using R Under development (unstable) (2025-12-23 r89222)
* using platform: aarch64-apple-darwin25.2.0
* R was compiled by
    Apple clang version 17.0.0 (clang-1700.6.3.2)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Tahoe 26.2
* using session charset: UTF-8
* using option ‘--no-stop-on-test-error’
* checking for file ‘tidyrules/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘tidyrules’ version ‘0.2.7’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘tidyrules’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking examples ... ERROR
Running examples in ‘tidyrules-Ex.R’ failed
The error most likely occurred in:

> ### Name: augment.rulelist
> ### Title: Augment a rulelist
> ### Aliases: augment.rulelist
> 
> ### ** Examples
> 
> # Examples for augment ------------------------------------------------------
> library("magrittr")
> 
> # C5 ----
> att = modeldata::attrition
> set.seed(100)
> train_index = sample(c(TRUE, FALSE), nrow(att), replace = TRUE)
> 
> model_c5 = C50::C5.0(Attrition ~., data = att[train_index, ], rules = TRUE)
> tidy_c5  =
+   model_c5 %>%
+   tidy() %>%
+   set_validation_data(att[!train_index, ], "Attrition")
> 
> tidy_c5
---- Rulelist --------------------------------
▶ Keys: trial_nbr
▶ Number of distinct keys: 1
▶ Number of rules: 23
▶ Model type: C5
▶ Estimation type: classification
▶ Is validation data set: TRUE


   rule_nbr trial_nbr LHS                         RHS   support confidence  lift
      <int>     <int> <chr>                       <fct>   <int>      <dbl> <dbl>
 1        1         1 ( Age > 30 ) & ( DistanceF… No         69      0.986   1.2
 2        2         1 ( DistanceFromHome <= 12 )… No        149      0.960   1.1
 3        3         1 ( Department == 'Research_… No        211      0.953   1.1
 4        4         1 ( Age > 30 ) & ( DistanceF… No        249      0.948   1.1
 5        5         1 ( JobInvolvement %in% c('M… No        353      0.944   1.1
 6        6         1 ( OverTime == 'No' ) & ( S… No        263      0.943   1.1
 7        7         1 ( Education %in% c('Master… No        101      0.942   1.1
 8        8         1 ( OverTime == 'No' ) & ( R… No         95      0.938   1.1
 9        9         1 ( BusinessTravel %in% c('N… No        352      0.915   1.1
10       10         1 ( Education %in% c('Below_… No        265      0.910   1.1
# ℹ 13 more rows
----------------------------------------------> 
> augment(tidy_c5) %>%
+   tidytable::unnest(augmented_stats, names_sep = "__") %>%
+   tidytable::glimpse()
Rows: 23
Columns: 10
$ rule_nbr                    <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,…
$ trial_nbr                   <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ LHS                         <chr> "( Age > 30 ) & ( DistanceFromHome <= 12 )…
$ RHS                         <fct> No, No, No, No, No, No, No, No, No, No, Ye…
$ support                     <int> 69, 149, 211, 249, 353, 263, 101, 95, 352,…
$ confidence                  <dbl> 0.9859155, 0.9603000, 0.9531000, 0.9482000…
$ lift                        <dbl> 1.2, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.…
$ augmented_stats__support    <dbl> 77, 122, 245, 282, 376, 305, 84, 111, 390,…
$ augmented_stats__confidence <dbl> 0.9220779, 0.9098361, 0.9346939, 0.9113475…
$ augmented_stats__lift       <dbl> 9.3667749, 1.0091812, 1.0367533, 1.0108577…
> 
> # augment with custom aggregator
> augment(tidy_c5,output_counts = list(table(Attrition))) %>%
+   tidytable::unnest(augmented_stats, names_sep = "__") %>%
+   tidytable::glimpse()
Rows: 23
Columns: 11
$ rule_nbr                       <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, …
$ trial_nbr                      <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ LHS                            <chr> "( Age > 30 ) & ( DistanceFromHome <= 1…
$ RHS                            <fct> No, No, No, No, No, No, No, No, No, No,…
$ support                        <int> 69, 149, 211, 249, 353, 263, 101, 95, 3…
$ confidence                     <dbl> 0.9859155, 0.9603000, 0.9531000, 0.9482…
$ lift                           <dbl> 1.2, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1,…
$ augmented_stats__support       <dbl> 77, 122, 245, 282, 376, 305, 84, 111, 3…
$ augmented_stats__confidence    <dbl> 0.9220779, 0.9098361, 0.9346939, 0.9113…
$ augmented_stats__output_counts <list> <<table[2]>>, <<table[2]>>, <<table[2]…
$ augmented_stats__lift          <dbl> 9.3667749, 1.0091812, 1.0367533, 1.0108…
> 
> # rpart ----
> set.seed(100)
> train_index = sample(c(TRUE, FALSE), nrow(iris), replace = TRUE)
> 
> model_class_rpart = rpart::rpart(Species ~ ., data = iris[train_index, ])
> tidy_class_rpart  = tidy(model_class_rpart) %>%
+   set_validation_data(iris[!train_index, ], "Species")
> tidy_class_rpart
---- Rulelist --------------------------------
▶ Keys: NULL
▶ Number of rules: 3
▶ Model type: rpart
▶ Estimation type: classification
▶ Is validation data set: TRUE


  rule_nbr LHS                                    RHS   support confidence  lift
     <int> <chr>                                  <fct>   <int>      <dbl> <dbl>
1        1 ( Petal.Width < 1.7 ) & ( Petal.Lengt… vers…      28      0.967  2.53
2        2 ( Petal.Width < 1.7 ) & ( Petal.Lengt… seto…      21      0.957  3.46
3        3 ( Petal.Width >= 1.7 )                 virg…      27      0.931  2.72
----------------------------------------------> 
> model_regr_rpart = rpart::rpart(Sepal.Length ~ ., data = iris[train_index, ])
> tidy_regr_rpart  = tidy(model_regr_rpart) %>%
+   set_validation_data(iris[!train_index, ], "Sepal.Length")
> tidy_regr_rpart
---- Rulelist --------------------------------
▶ Keys: NULL
▶ Number of rules: 5
▶ Model type: rpart
▶ Estimation type: regression
▶ Is validation data set: TRUE


  rule_nbr LHS                                                       RHS support
     <int> <chr>                                                   <dbl>   <int>
1        1 ( Petal.Length >= 4.25 ) & ( Petal.Length < 5.85 ) & (…  6.15      23
2        2 ( Petal.Length < 4.25 ) & ( Petal.Length < 1.65 )        4.99      17
3        3 ( Petal.Length < 4.25 ) & ( Petal.Length >= 1.65 )       5.6       16
4        4 ( Petal.Length >= 4.25 ) & ( Petal.Length < 5.85 ) & (…  6.53      12
5        5 ( Petal.Length >= 4.25 ) & ( Petal.Length >= 5.85 )      7.34       8
----------------------------------------------> 
> # augment (classification case)
> augment(tidy_class_rpart) %>%
+   tidytable::unnest(augmented_stats, names_sep = "__") %>%
+   tidytable::glimpse()
Rows: 3
Columns: 9
$ rule_nbr                    <int> 1, 2, 3
$ LHS                         <chr> "( Petal.Width < 1.7 ) & ( Petal.Length >=…
$ RHS                         <fct> versicolor, setosa, virginica
$ support                     <int> 28, 21, 27
$ confidence                  <dbl> 0.9666667, 0.9565217, 0.9310345
$ lift                        <dbl> 2.533333, 3.461698, 2.721485
$ augmented_stats__support    <dbl> 24, 29, 21
$ augmented_stats__confidence <dbl> 0.8333333, 1.0000000, 0.9523810
$ augmented_stats__lift       <dbl> 2.936508, 2.551724, 3.356009
> 
> # augment (regression case)
> augment(tidy_regr_rpart) %>%
+   tidytable::unnest(augmented_stats, names_sep = "__") %>%
+   tidytable::glimpse()
Rows: 5
Columns: 7
$ rule_nbr                 <int> 1, 2, 3, 4, 5
$ LHS                      <chr> "( Petal.Length >= 4.25 ) & ( Petal.Length < …
$ RHS                      <dbl> 6.147826, 4.988235, 5.600000, 6.533333, 7.337…
$ support                  <int> 23, 17, 16, 12, 8
$ augmented_stats__support <dbl> 20, 27, 13, 9, 5
$ augmented_stats__IQR     <dbl> 0.60, 0.45, 0.60, 0.50, 0.40
$ augmented_stats__RMSE    <dbl> 0.5085832, 0.3548713, 0.4497863, 0.3294215, 0…
> 
> # party ----
> pen = palmerpenguins::penguins %>%
+   tidytable::drop_na(bill_length_mm)
> set.seed(100)
> train_index = sample(c(TRUE, FALSE), nrow(pen), replace = TRUE)
> 
> model_class_party = partykit::ctree(species ~ ., data = pen[train_index, ])
> tidy_class_party  = tidy(model_class_party) %>%
+   set_validation_data(pen[!train_index, ], "species")
> tidy_class_party
---- Rulelist --------------------------------
▶ Keys: NULL
▶ Number of rules: 5
▶ Model type: constparty
▶ Estimation type: classification
▶ Is validation data set: TRUE


  rule_nbr LHS                   RHS   support confidence  lift terminal_node_id
     <int> <chr>                 <fct>   <dbl>      <dbl> <dbl> <chr>           
1        1 ( flipper_length_mm … Gent…      50      1      3.02 8               
2        2 ( flipper_length_mm … Adel…      73      0.986  2.08 3               
3        3 ( flipper_length_mm … Chin…      17      0.941  4.86 5               
4        4 ( flipper_length_mm … Chin…       7      0.714  3.69 9               
5        5 ( flipper_length_mm … Chin…      13      0.692  3.57 6               
----------------------------------------------> 
> model_regr_party =
+   partykit::ctree(bill_length_mm ~ ., data = pen[train_index, ])
> tidy_regr_party  = tidy(model_regr_party) %>%
+   set_validation_data(pen[!train_index, ], "bill_length_mm")
Error in `[.data.table`(~.df, , `:=`(average = weighted.mean(response,  : 
  Internal error in assign: input dt has not been allocated enough column slots. l=3, tl=3, adding 1. Please report to the data.table issues tracker.
Calls: %>% ... <Anonymous> -> mutate.tidytable -> eval_tidy -> [ -> [.data.table
Execution halted
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’ [13s/13s]
 [13s/13s] ERROR
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(tidyrules)
  > 
  > test_check("tidyrules")
  Saving _problems/test-party-19.R
  ---- Rulelist --------------------------------
  ▶ Keys: trial_nbr
  ▶ Number of distinct keys: 1
  ▶ Number of rules: 24
  ▶ Model type: C5
  ▶ Estimation type: classification
  ▶ Is validation data set: TRUE
  
  
     rule_nbr trial_nbr LHS                         RHS   support confidence  lift
        <int>     <int> <chr>                       <fct>   <int>      <dbl> <dbl>
   1        1         1 ( JobLevel <= 1 ) & ( Mont… Yes        16      0.944   5.9
   2        2         1 ( EnvironmentSatisfaction … No        521      0.941   1.1
   3        3         1 ( DailyRate <= 722 ) & ( J… Yes        13      0.933   5.8
   4        4         1 ( JobRole == 'Research_Sci… No        195      0.924   1.1
   5        5         1 ( EnvironmentSatisfaction … Yes         9      0.909   5.6
   6        6         1 ( EnvironmentSatisfaction … Yes         9      0.909   5.6
   7        7         1 ( JobRole %in% c('Laborato… Yes        14      0.875   5.4
   8        8         1 ( JobRole == 'Laboratory_T… Yes         6      0.875   5.4
   9        9         1 ( Department == 'Sales' ) … Yes        13      0.867   5.4
  10       10         1 ( TotalWorkingYears > 2 )   No       1347      0.864   1  
  # ℹ 14 more rows
  -------------------------------------------------- Rulelist --------------------------------
  ▶ Keys: trial_nbr
  ▶ Number of distinct keys: 1
  ▶ Number of rules: 24
  ▶ Model type: C5
  ▶ Estimation type: classification
  ▶ Is validation data set: FALSE
  
  
     rule_nbr trial_nbr LHS                         RHS   support confidence  lift
        <int>     <int> <chr>                       <fct>   <int>      <dbl> <dbl>
   1        1         1 ( JobLevel <= 1 ) & ( Mont… Yes        16      0.944   5.9
   2        2         1 ( EnvironmentSatisfaction … No        521      0.941   1.1
   3        3         1 ( DailyRate <= 722 ) & ( J… Yes        13      0.933   5.8
   4        4         1 ( JobRole == 'Research_Sci… No        195      0.924   1.1
   5        5         1 ( EnvironmentSatisfaction … Yes         9      0.909   5.6
   6        6         1 ( EnvironmentSatisfaction … Yes         9      0.909   5.6
   7        7         1 ( JobRole %in% c('Laborato… Yes        14      0.875   5.4
   8        8         1 ( JobRole == 'Laboratory_T… Yes         6      0.875   5.4
   9        9         1 ( Department == 'Sales' ) … Yes        13      0.867   5.4
  10       10         1 ( TotalWorkingYears > 2 )   No       1347      0.864   1  
  # ℹ 14 more rows
  ----------------------------------------------[ FAIL 1 | WARN 1 | SKIP 0 | PASS 76 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-party.R:19:1'): (code run outside of `test_that()`) ────────────
  Error in ``[.data.table`(~.df, , `:=`(average = weighted.mean(response, weight, na.rm = TRUE)), by = "terminal_node_id", keyby = FALSE)`: Internal error in assign: input dt has not been allocated enough column slots. l=3, tl=3, adding 1. Please report to the data.table issues tracker.
  Backtrace:
       ▆
    1. ├─generics::tidy(model_party_re) at test-party.R:19:1
    2. ├─tidyrules:::tidy.constparty(model_party_re)
    3. │ └─... %>% ...
    4. ├─tidytable::select(...)
    5. ├─tidytable::mutate(., rule_nbr = 1:nrow(.))
    6. ├─tidytable::arrange(., RMSE, desc(support))
    7. ├─tidytable::left_join(., rules_df, by = "terminal_node_id")
    8. │ ├─c(x, y, on, selection) %<-% ...
    9. │ └─tidytable:::join_prep(x, y, by, keep, suffix, "left")
   10. │   └─base::is.data.frame(x)
   11. ├─tidytable::summarise(...)
   12. ├─tidytable::mutate(...)
   13. ├─tidytable:::mutate.tidytable(...)
   14. │ └─rlang::eval_tidy(dt_expr, .df, dt_env)
   15. ├─...[]
   16. └─data.table:::`[.data.table`(...)
  
  [ FAIL 1 | WARN 1 | SKIP 0 | PASS 76 ]
  Error:
  ! Test failures.
  Execution halted
* checking PDF version of manual ... OK
* checking HTML version of manual ... OK
* checking for detritus in the temp directory ... OK
* DONE

Status: 2 ERRORs
See
  ‘/Users/ripley/R/packages/tests-devel/tidyrules.Rcheck/00check.log’
for details.

       40.22 real        34.05 user         4.89 sys