R version 4.3.2 (2023-10-31) -- "Eye Holes"
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> library(tidymodels)
── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ──
✔ broom        1.0.5          ✔ recipes      1.0.10    
✔ dials        1.2.1          ✔ rsample      1.2.0     
✔ dplyr        1.1.4          ✔ tibble       3.2.1     
✔ ggplot2      3.5.0          ✔ tidyr        1.3.1     
✔ infer        1.0.5          ✔ tune         1.2.0     
✔ modeldata    1.3.0          ✔ workflows    1.1.4     
✔ parsnip      1.2.0          ✔ workflowsets 1.0.1.9001
✔ purrr        1.0.2          ✔ yardstick    1.3.0     
── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
✖ purrr::discard() masks scales::discard()
✖ dplyr::filter()  masks stats::filter()
✖ dplyr::lag()     masks stats::lag()
✖ recipes::step()  masks stats::step()
• Learn how to get started at https://www.tidymodels.org/start/
> library(scales)
> library(censored)
Loading required package: survival
> library(sessioninfo)
> library(testthat)

Attaching package: ‘testthat’

The following object is masked from ‘package:tidyr’:

    matches

The following object is masked from ‘package:rsample’:

    matches

The following object is masked from ‘package:purrr’:

    is_null

The following object is masked from ‘package:dplyr’:

    matches

> # also will require prodlim, mboost, kknn, and kernlab
> 
> # ------------------------------------------------------------------------------
> # "mt_*" test objects used in test-predictions.R, test-extract.R, and test-autoplot.R
> 
> set.seed(455)
> folds <- vfold_cv(mtcars, v = 5)
> 
> simple_rec <- recipe(mpg ~ ., data = mtcars)
> 
> form <- mpg ~ .
> 
> spline_rec <-
+   recipe(mpg ~ ., data = mtcars) %>%
+   step_normalize(all_predictors()) %>%
+   step_bs(disp, deg_free = tune())
> 
> lm_mod <- linear_reg() %>% set_engine("lm")
> 
> knn_mod <-
+   nearest_neighbor(mode = "regression", neighbors = tune()) %>%
+   set_engine("kknn")
> 
> knn_mod_two <-
+   nearest_neighbor(mode = "regression", neighbors = tune("K"), weight_func = tune()) %>%
+   set_engine("kknn")
> 
> get_coefs  <- function(x) {
+   x %>%
+     extract_fit_parsnip() %>%
+     tidy()
+ }
> 
> verb <- FALSE
> g_ctrl <- control_grid(verbose = verb, save_pred = TRUE, extract = get_coefs)
> b_ctrl <- control_bayes(verbose = verb, save_pred = TRUE, extract = get_coefs)
> 
> # ------------------------------------------------------------------------------
> 
> mt_spln_lm <-
+   workflow() %>%
+   add_recipe(spline_rec) %>%
+   add_model(lm_mod)
> 
> mt_spln_knn <-
+   workflow() %>%
+   add_recipe(spline_rec) %>%
+   add_model(knn_mod)
> 
> mt_knn <-
+   workflow() %>%
+   add_recipe(simple_rec) %>%
+   add_model(knn_mod)
> 
> # ------------------------------------------------------------------------------
> 
> set.seed(8825)
> mt_spln_lm_grid <-
+   tune_grid(mt_spln_lm,
+             resamples = folds,
+             control = g_ctrl)
→ A | warning: prediction from rank-deficient fit; consider predict(., rankdeficient="NA")

There were issues with some computations   A: x1

                                                 
→ B | warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
There were issues with some computations   A: x1

There were issues with some computations   A: x1   B: x4

There were issues with some computations   A: x2   B: x10

There were issues with some computations   A: x2   B: x11

There were issues with some computations   A: x2   B: x16

There were issues with some computations   A: x2   B: x20
> 
> set.seed(8825)
> mt_spln_lm_bo <-
+   tune_bayes(
+     mt_spln_lm,
+     resamples = folds,
+     iter = 3,
+     control = b_ctrl
+   )
→ A | warning: some 'x' values beyond boundary knots may cause ill-conditioned bases

There were issues with some computations   A: x1

There were issues with some computations   A: x5

There were issues with some computations   A: x6

There were issues with some computations   A: x11

There were issues with some computations   A: x13

There were issues with some computations   A: x14

There were issues with some computations   A: x16

There were issues with some computations   A: x16
> 
> # ------------------------------------------------------------------------------
> 
> set.seed(8825)
> mt_spln_knn_grid <-
+   tune_grid(
+     mt_spln_knn,
+     resamples = folds,
+     grid = grid_regular(extract_parameter_set_dials(mt_spln_knn)),
+     control = g_ctrl
+   )
→ A | error:   No tidy method for objects of class train.kknn

There were issues with some computations   A: x2

                                                 
→ B | warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
There were issues with some computations   A: x2

There were issues with some computations   A: x6   B: x3

There were issues with some computations   A: x12   B: x3

There were issues with some computations   A: x15   B: x6
> 
> set.seed(8825)
> mt_spln_knn_bo <-
+   tune_bayes(mt_spln_knn,
+              resamples = folds,
+              iter = 3,
+              control = b_ctrl)
→ A | error:   No tidy method for objects of class train.kknn

There were issues with some computations   A: x1

There were issues with some computations   A: x4

                                                 
→ B | warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
There were issues with some computations   A: x4

There were issues with some computations   A: x8   B: x2

There were issues with some computations   A: x14   B: x5

There were issues with some computations   A: x20   B: x5

There were issues with some computations   A: x25   B: x10

There were issues with some computations   A: x26   B: x10

There were issues with some computations   A: x31   B: x12

There were issues with some computations   A: x36   B: x14

There were issues with some computations   A: x37   B: x14

There were issues with some computations   A: x40   B: x16
> 
> set.seed(8825)
> mt_spln_knn_bo_sep <-
+   tune_bayes(knn_mod_two,
+              spline_rec,
+              resamples = folds,
+              iter = 3,
+              control = b_ctrl)
→ A | error:   No tidy method for objects of class train.kknn

There were issues with some computations   A: x1

There were issues with some computations   A: x6

                                                 
→ B | warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
There were issues with some computations   A: x6

There were issues with some computations   A: x12   B: x5

There were issues with some computations   A: x18   B: x5

There were issues with some computations   A: x24   B: x8
! The Gaussian process model is being fit using 12 features but only has 5
  data points to do so. This may cause errors or a poor model fit.

                                                          
→ C | warning: did not converge in 10 iterations
There were issues with some computations   A: x24   B: x8

There were issues with some computations   A: x25   B: x10   C: x1

There were issues with some computations   A: x26   B: x10   C: x1

There were issues with some computations   A: x29   B: x11   C: x1
! The Gaussian process model is being fit using 12 features but only has 6
  data points to do so. This may cause errors or a poor model fit.

There were issues with some computations   A: x31   B: x12   C: x1
! The Gaussian process model is being fit using 12 features but only has 7
  data points to do so. This may cause errors or a poor model fit.

There were issues with some computations   A: x36   B: x14   C: x1

There were issues with some computations   A: x37   B: x14   C: x1

There were issues with some computations   A: x40   B: x16   C: x1
> 
> # ------------------------------------------------------------------------------
> 
> set.seed(8825)
> mt_knn_grid <- tune_grid(mt_knn, resamples = folds, control = g_ctrl)
→ A | error:   No tidy method for objects of class train.kknn

There were issues with some computations   A: x2

There were issues with some computations   A: x5
> 
> set.seed(8825)
> mt_knn_bo <-
+   tune_bayes(mt_knn,
+              resamples = folds,
+              iter = 3,
+              control = b_ctrl)
→ A | error:   No tidy method for objects of class train.kknn

There were issues with some computations   A: x1

There were issues with some computations   A: x6

There were issues with some computations   A: x11

There were issues with some computations   A: x17

There were issues with some computations   A: x20
> 
> # ------------------------------------------------------------------------------
> 
> save(
+   list = grep("^mt_", ls(), value = TRUE),
+   file = test_path("data", "test_objects.RData"),
+   version = 2,
+   compress = "xz"
+ )
> 
> # ------------------------------------------------------------------------------
> # "knn_*" test objects used in test-predictions.R, test-autoplot.R, test-GP.R
> # and test-select_best.R
> 
> data(two_class_dat, package = "modeldata")
> set.seed(7898)
> data_folds <- vfold_cv(two_class_dat, repeats = 5)
> 
> two_class_rec <-
+   recipe(Class ~ ., data = two_class_dat) %>%
+   step_normalize(A, B)
> 
> knn_model <-
+   nearest_neighbor(
+     mode = "classification",
+     neighbors = tune("K"),
+     weight_func = tune(),
+     dist_power = tune("exponent")
+   ) %>%
+   set_engine("kknn")
> 
> two_class_wflow <-
+   workflow() %>%
+   add_recipe(two_class_rec) %>%
+   add_model(knn_model)
> 
> two_class_set <-
+   extract_parameter_set_dials(two_class_wflow) %>%
+   update(K = neighbors(c(1, 50))) %>%
+   update(exponent = dist_power(c(1 / 10, 2)))
> 
> set.seed(2494)
> two_class_grid <-
+   two_class_set %>%
+   grid_max_entropy(size = 10)
> 
> class_metrics <- metric_set(roc_auc, accuracy, kap, mcc)
> 
> knn_results <-
+   tune_grid(
+     two_class_wflow,
+     resamples = data_folds,
+     grid = two_class_grid,
+     metrics = class_metrics
+   )
> 
> 
> knn_set <- two_class_set
> 
> knn_gp <-
+   tune:::fit_gp(dat = collect_metrics(knn_results),
+                 pset = knn_set,
+                 metric = "accuracy",
+                 control = control_bayes()
+   )
! The Gaussian process model is being fit using 12 features but only has 10
  data points to do so. This may cause errors or a poor model fit.
> 
> saveRDS(
+   knn_results,
+   file = testthat::test_path("data", "knn_results.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> saveRDS(
+   two_class_set,
+   file = testthat::test_path("data", "knn_set.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> saveRDS(
+   two_class_grid,
+   file = testthat::test_path("data", "knn_grid.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> saveRDS(
+   knn_set,
+   file = testthat::test_path("data", "knn_set.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> saveRDS(
+   knn_gp,
+   file = testthat::test_path("data", "knn_gp.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> # ------------------------------------------------------------------------------
> # "svm_*" test objects used in numerous test files
> 
> svm_model <-
+   svm_poly(
+     mode = "classification",
+     cost = tune(),
+     degree = tune("%^*#"),
+     scale_factor = tune()
+   ) %>%
+   set_engine("kernlab")
> 
> two_class_wflow <-
+   workflow() %>%
+   add_recipe(two_class_rec) %>%
+   add_model(svm_model)
> 
> two_class_set <-
+   extract_parameter_set_dials(two_class_wflow) %>%
+   update(cost = cost(c(-10, 4)))
> 
> set.seed(2494)
> two_class_grid <-
+   two_class_set %>%
+   grid_max_entropy(size = 5)
> 
> class_only <- metric_set(accuracy, kap, mcc)
> 
> svm_results <-
+   tune_grid(
+     two_class_wflow,
+     resamples = data_folds,
+     grid = two_class_grid,
+     metrics = class_only,
+     control = control_grid(save_pred = TRUE)
+   )
> 
> saveRDS(
+   svm_results,
+   file = testthat::test_path("data", "svm_results.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> two_class_reg_grid <-
+   two_class_set %>%
+   grid_regular(levels = c(5, 4, 2))
> 
> svm_reg_results <-
+   tune_grid(
+     two_class_wflow,
+     resamples = data_folds,
+     grid = two_class_reg_grid,
+     metrics = class_only,
+     control = control_grid(save_pred = TRUE)
+   )
> 
> saveRDS(
+   svm_reg_results,
+   file = testthat::test_path("data", "svm_reg_results.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> # ------------------------------------------------------------------------------
> 
> set.seed(7898)
> data_folds <- vfold_cv(mtcars, repeats = 2)
> 
> # ------------------------------------------------------------------------------
> # "rcv_results" used in test-autoplot.R, test-select_best.R, and test-estimate.R
> 
> base_rec <-
+   recipe(mpg ~ ., data = mtcars) %>%
+   step_normalize(all_predictors())
> 
> disp_rec <-
+   base_rec %>%
+   step_bs(disp, degree = tune(), deg_free = tune()) %>%
+   step_bs(wt, degree = tune("wt degree"), deg_free = tune("wt df"))
> 
> lm_model <-
+   linear_reg(mode = "regression") %>%
+   set_engine("lm")
> 
> cars_wflow <-
+   workflow() %>%
+   add_recipe(disp_rec) %>%
+   add_model(lm_model)
> 
> cars_set <-
+   cars_wflow %>%
+   parameters %>%
+   update(degree = degree_int(1:2)) %>%
+   update(deg_free = deg_free(c(2, 10))) %>%
+   update(`wt degree` = degree_int(1:2)) %>%
+   update(`wt df` = deg_free(c(2, 10)))
Warning message:
`parameters.workflow()` was deprecated in tune 0.1.6.9003.
ℹ Please use `hardhat::extract_parameter_set_dials()` instead. 
> 
> set.seed(255)
> cars_grid <-
+   cars_set %>%
+   grid_regular(levels = c(3, 2, 3, 2))
> 
> 
> rcv_results <-
+   tune_grid(
+     cars_wflow,
+     resamples = data_folds,
+     grid = cars_grid,
+     control = control_grid(verbose = FALSE, save_pred = TRUE)
+   )
→ A | warning: prediction from rank-deficient fit; consider predict(., rankdeficient="NA")

There were issues with some computations   A: x1

There were issues with some computations   A: x3

There were issues with some computations   A: x4

                                                 
→ B | warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
There were issues with some computations   A: x4

There were issues with some computations   A: x4   B: x3

There were issues with some computations   A: x4   B: x9

There were issues with some computations   A: x4   B: x14

                                                          
→ C | warning: some 'x' values beyond boundary knots may cause ill-conditioned bases, prediction from rank-deficient fit; consider predict(., rankdeficient="NA")
There were issues with some computations   A: x4   B: x14

There were issues with some computations   A: x4   B: x18   C: x2

There were issues with some computations   A: x4   B: x23   C: x2

There were issues with some computations   A: x4   B: x29   C: x2

There were issues with some computations   A: x4   B: x32   C: x4

There were issues with some computations   A: x4   B: x33   C: x4

There were issues with some computations   A: x4   B: x39   C: x4

There were issues with some computations   A: x4   B: x44   C: x4

There were issues with some computations   A: x4   B: x49   C: x4

There were issues with some computations   A: x4   B: x54   C: x4

There were issues with some computations   A: x4   B: x60   C: x4

There were issues with some computations   A: x4   B: x65   C: x4

There were issues with some computations   A: x4   B: x71   C: x4

There were issues with some computations   A: x4   B: x76   C: x4

There were issues with some computations   A: x4   B: x82   C: x4

There were issues with some computations   A: x4   B: x88   C: x4

There were issues with some computations   A: x4   B: x93   C: x4

There were issues with some computations   A: x4   B: x99   C: x4

There were issues with some computations   A: x4   B: x104   C: x4

There were issues with some computations   A: x4   B: x105   C: x4

There were issues with some computations   A: x4   B: x111   C: x4

There were issues with some computations   A: x4   B: x117   C: x4

There were issues with some computations   A: x4   B: x122   C: x4

There were issues with some computations   A: x4   B: x128   C: x4

There were issues with some computations   A: x4   B: x133   C: x4

There were issues with some computations   A: x4   B: x139   C: x4

There were issues with some computations   A: x4   B: x144   C: x4

There were issues with some computations   A: x4   B: x150   C: x4

There were issues with some computations   A: x4   B: x154   C: x5

There were issues with some computations   A: x4   B: x159   C: x6

There were issues with some computations   A: x4   B: x164   C: x6

There were issues with some computations   A: x4   B: x170   C: x6

There were issues with some computations   A: x5   B: x172   C: x8

There were issues with some computations   A: x7   B: x172   C: x8

There were issues with some computations   A: x8   B: x172   C: x8

There were issues with some computations   A: x8   B: x173   C: x8

There were issues with some computations   A: x8   B: x178   C: x8

There were issues with some computations   A: x8   B: x184   C: x8

There were issues with some computations   A: x8   B: x189   C: x8

There were issues with some computations   A: x8   B: x195   C: x8

There were issues with some computations   A: x8   B: x200   C: x8

There were issues with some computations   A: x8   B: x206   C: x8

There were issues with some computations   A: x8   B: x209   C: x8

There were issues with some computations   A: x8   B: x215   C: x8

There were issues with some computations   A: x8   B: x220   C: x8

There were issues with some computations   A: x8   B: x226   C: x8

There were issues with some computations   A: x8   B: x229   C: x8

There were issues with some computations   A: x8   B: x235   C: x8

There were issues with some computations   A: x8   B: x241   C: x8

There were issues with some computations   A: x8   B: x245   C: x8

There were issues with some computations   A: x8   B: x246   C: x8

There were issues with some computations   A: x8   B: x251   C: x8

There were issues with some computations   A: x8   B: x257   C: x8

There were issues with some computations   A: x8   B: x263   C: x8

There were issues with some computations   A: x8   B: x269   C: x8

There were issues with some computations   A: x8   B: x274   C: x8

There were issues with some computations   A: x8   B: x280   C: x8

There were issues with some computations   A: x8   B: x280   C: x8
> 
> saveRDS(
+   rcv_results,
+   file = testthat::test_path("data", "rcv_results.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> 
> # ------------------------------------------------------------------------------
> # Object classed with `resample_results` for use in vctrs/dplyr tests
> 
> set.seed(6735)
> 
> folds <- vfold_cv(mtcars, v = 3)
> 
> rec <- recipe(mpg ~ ., data = mtcars)
> 
> mod <- linear_reg() %>%
+   set_engine("lm")
> 
> lm_resamples <- fit_resamples(mod, rec, folds)
> 
> lm_resamples
# Resampling results
# 3-fold cross-validation 
# A tibble: 3 × 4
  splits          id    .metrics         .notes          
  <list>          <chr> <list>           <list>          
1 <split [21/11]> Fold1 <tibble [2 × 4]> <tibble [0 × 3]>
2 <split [21/11]> Fold2 <tibble [2 × 4]> <tibble [0 × 3]>
3 <split [22/10]> Fold3 <tibble [2 × 4]> <tibble [0 × 3]>
> 
> saveRDS(
+   lm_resamples,
+   file = testthat::test_path("data", "lm_resamples.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> # ------------------------------------------------------------------------------
> # Object classed with `iteration_results` for use in vctrs/dplyr tests
> 
> set.seed(7898)
> folds <- vfold_cv(mtcars, v = 2)
> 
> rec <- recipe(mpg ~ ., data = mtcars) %>%
+   step_normalize(all_predictors()) %>%
+   step_ns(disp, deg_free = tune())
> 
> mod <- linear_reg(mode = "regression") %>%
+   set_engine("lm")
> 
> wflow <- workflow() %>%
+   add_recipe(rec) %>%
+   add_model(mod)
> 
> set.seed(2934)
> lm_bayes <- tune_bayes(wflow, folds, initial = 4, iter = 3)
→ A | warning: prediction from rank-deficient fit; consider predict(., rankdeficient="NA")

There were issues with some computations   A: x2

There were issues with some computations   A: x5

There were issues with some computations   A: x6
> 
> saveRDS(
+   lm_bayes,
+   file = testthat::test_path("data", "lm_bayes.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> # ------------------------------------------------------------------------------
> # A single survival model
> 
> set.seed(1)
> sim_dat <- prodlim::SimSurv(200) %>%
+   mutate(event_time = Surv(time, event)) %>%
+   select(event_time, X1, X2)
> 
> set.seed(2)
> sim_rs <- vfold_cv(sim_dat)
> 
> time_points <- c(10, 1, 5, 15)
> 
> boost_spec <-
+   boost_tree(trees = tune()) %>%
+   set_mode("censored regression") %>%
+   set_engine("mboost")
> 
> srv_mtr <-
+   metric_set(
+     brier_survival,
+     roc_auc_survival,
+     brier_survival_integrated,
+     concordance_survival
+   )
> 
> set.seed(2193)
> surv_boost_tree_res <-
+   boost_spec %>%
+   tune_grid(
+     event_time ~ X1 + X2,
+     resamples = sim_rs,
+     grid  = tibble(trees = c(1, 5, 10, 20, 100)),
+     metrics = srv_mtr,
+     eval_time = time_points
+   )
> 
> saveRDS(
+   surv_boost_tree_res,
+   file = testthat::test_path("data", "surv_boost_tree_res.rds"),
+   version = 2,
+   compress = "xz"
+ )
> 
> 
> # ------------------------------------------------------------------------------
> 
> sessioninfo::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.2 (2023-10-31)
 os       macOS Sonoma 14.4
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2024-03-20
 pandoc   3.1.11 @ /opt/homebrew/bin/pandoc

─ Packages ───────────────────────────────────────────────────────────────────
 package      * version    date (UTC) lib source
 backports      1.4.1      2021-12-13 [1] CRAN (R 4.3.0)
 brio           1.1.4      2023-12-10 [1] CRAN (R 4.3.1)
 broom        * 1.0.5      2023-06-09 [1] CRAN (R 4.3.0)
 censored     * 0.3.0      2024-01-31 [1] CRAN (R 4.3.1)
 class          7.3-22     2023-05-03 [2] CRAN (R 4.3.2)
 cli            3.6.2      2023-12-11 [1] CRAN (R 4.3.1)
 codetools      0.2-19     2023-02-01 [2] CRAN (R 4.3.2)
 colorspace     2.1-0      2023-01-23 [1] CRAN (R 4.3.0)
 data.table     1.15.2     2024-02-29 [1] CRAN (R 4.3.1)
 dials        * 1.2.1      2024-02-22 [1] CRAN (R 4.3.1)
 DiceDesign     1.10       2023-12-07 [1] CRAN (R 4.3.2)
 digest         0.6.35     2024-03-11 [1] CRAN (R 4.3.1)
 dplyr        * 1.1.4      2023-11-17 [1] CRAN (R 4.3.1)
 ellipsis       0.3.2      2021-04-29 [1] CRAN (R 4.3.0)
 fansi          1.0.6      2023-12-08 [1] CRAN (R 4.3.1)
 foreach        1.5.2      2022-02-02 [1] CRAN (R 4.3.0)
 Formula        1.2-5      2023-02-24 [1] CRAN (R 4.3.0)
 furrr          0.3.1      2022-08-15 [1] CRAN (R 4.3.0)
 future         1.33.1     2023-12-22 [1] CRAN (R 4.3.1)
 future.apply   1.11.1     2023-12-21 [1] CRAN (R 4.3.1)
 generics       0.1.3      2022-07-05 [1] CRAN (R 4.3.0)
 ggplot2      * 3.5.0      2024-02-23 [1] CRAN (R 4.3.1)
 globals        0.16.3     2024-03-08 [1] CRAN (R 4.3.1)
 glue           1.7.0      2024-01-09 [1] CRAN (R 4.3.1)
 gower          1.0.1      2022-12-22 [1] CRAN (R 4.3.0)
 GPfit          1.0-8      2019-02-08 [1] CRAN (R 4.3.0)
 gtable         0.3.4      2023-08-21 [1] CRAN (R 4.3.0)
 hardhat        1.3.1      2024-02-02 [1] CRAN (R 4.3.1)
 igraph         2.0.3      2024-03-13 [1] CRAN (R 4.3.1)
 infer        * 1.0.5      2023-09-06 [1] CRAN (R 4.3.0)
 inum           1.0-5      2023-03-09 [1] CRAN (R 4.3.0)
 ipred          0.9-14     2023-03-09 [1] CRAN (R 4.3.0)
 iterators      1.0.14     2022-02-05 [1] CRAN (R 4.3.0)
 kernlab      * 0.9-32     2023-01-31 [1] CRAN (R 4.3.0)
 kknn         * 1.3.1      2016-03-26 [1] CRAN (R 4.3.0)
 lattice        0.22-5     2023-10-24 [1] CRAN (R 4.3.1)
 lava           1.8.0      2024-03-05 [1] CRAN (R 4.3.1)
 lhs            1.1.6      2022-12-17 [1] CRAN (R 4.3.0)
 libcoin        1.0-10     2023-09-27 [1] CRAN (R 4.3.1)
 lifecycle      1.0.4      2023-11-07 [1] CRAN (R 4.3.1)
 listenv        0.9.1      2024-01-29 [1] CRAN (R 4.3.1)
 lubridate      1.9.3      2023-09-27 [1] CRAN (R 4.3.1)
 magrittr       2.0.3      2022-03-30 [1] CRAN (R 4.3.0)
 MASS           7.3-60     2023-05-04 [2] CRAN (R 4.3.2)
 Matrix         1.6-4      2023-11-30 [1] CRAN (R 4.3.2)
 mboost       * 2.9-9      2023-12-07 [1] CRAN (R 4.3.2)
 modeldata    * 1.3.0      2024-01-21 [1] CRAN (R 4.3.1)
 modelenv       0.1.1      2023-03-08 [1] CRAN (R 4.3.0)
 munsell        0.5.0      2018-06-12 [1] CRAN (R 4.3.0)
 mvtnorm        1.2-4      2023-11-27 [1] CRAN (R 4.3.1)
 nnet           7.3-19     2023-05-03 [2] CRAN (R 4.3.2)
 nnls           1.5        2023-09-11 [1] CRAN (R 4.3.0)
 parallelly     1.37.1     2024-02-29 [1] CRAN (R 4.3.1)
 parsnip      * 1.2.0      2024-02-16 [1] CRAN (R 4.3.1)
 partykit       1.2-20     2023-04-14 [1] CRAN (R 4.3.0)
 pillar         1.9.0      2023-03-22 [1] CRAN (R 4.3.0)
 pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.3.0)
 pkgload        1.3.4      2024-01-16 [1] CRAN (R 4.3.1)
 prodlim        2023.08.28 2023-08-28 [1] CRAN (R 4.3.0)
 purrr        * 1.0.2      2023-08-10 [1] CRAN (R 4.3.0)
 quadprog       1.5-8      2019-11-20 [1] CRAN (R 4.3.0)
 R6             2.5.1      2021-08-19 [1] CRAN (R 4.3.0)
 Rcpp           1.0.12     2024-01-09 [1] CRAN (R 4.3.1)
 recipes      * 1.0.10     2024-02-18 [1] CRAN (R 4.3.1)
 rlang          1.1.3      2024-01-10 [1] CRAN (R 4.3.1)
 rpart          4.1.23     2023-12-05 [1] CRAN (R 4.3.1)
 rsample      * 1.2.0      2023-08-23 [1] CRAN (R 4.3.0)
 rstudioapi     0.15.0     2023-07-07 [1] CRAN (R 4.3.0)
 scales       * 1.3.0      2023-11-28 [1] CRAN (R 4.3.1)
 sessioninfo  * 1.2.2      2021-12-06 [1] CRAN (R 4.3.0)
 stabs        * 0.6-4      2021-01-29 [1] CRAN (R 4.3.0)
 survival     * 3.5-7      2023-08-14 [2] CRAN (R 4.3.2)
 testthat     * 3.2.1      2023-12-02 [1] CRAN (R 4.3.1)
 tibble       * 3.2.1      2023-03-20 [1] CRAN (R 4.3.0)
 tidymodels   * 1.1.1      2023-08-24 [1] CRAN (R 4.3.0)
 tidyr        * 1.3.1      2024-01-24 [1] CRAN (R 4.3.1)
 tidyselect     1.2.1      2024-03-11 [1] CRAN (R 4.3.1)
 timechange     0.3.0      2024-01-18 [1] CRAN (R 4.3.1)
 timeDate       4032.109   2023-12-14 [1] CRAN (R 4.3.1)
 tune         * 1.2.0      2024-03-20 [1] local
 utf8           1.2.4      2023-10-22 [1] CRAN (R 4.3.1)
 vctrs          0.6.5      2023-12-01 [1] CRAN (R 4.3.1)
 withr          3.0.0      2024-01-16 [1] CRAN (R 4.3.1)
 workflows    * 1.1.4      2024-02-19 [1] CRAN (R 4.3.1)
 workflowsets * 1.0.1.9001 2024-01-25 [1] Github (tidymodels/workflowsets@1187448)
 yardstick    * 1.3.0      2024-01-19 [1] CRAN (R 4.3.1)

 [1] /Users/max/Library/R/arm64/4.3/library
 [2] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library

──────────────────────────────────────────────────────────────────────────────
> 
> if (!interactive()) {
+   q("no")
+ }
> proc.time()
   user  system elapsed 
179.967   1.245 181.806