R Under development (unstable) (2026-03-24 r89696 ucrt) -- "Unsuffered Consequences"
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> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(wpor)
> 
> test_check("wpor")

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

-- Attaching packages -------------------------------------- tidymodels 1.4.1 --
v broom        1.0.12     v rsample      1.3.2 
v dials        1.4.2      v tailor       0.1.0 
v ggplot2      4.0.2      v tidyr        1.3.2 
v infer        1.1.0      v tune         2.0.1 
v modeldata    1.5.1      v workflows    1.3.0 
v parsnip      1.4.1      v workflowsets 1.1.1 
v purrr        1.2.1      v yardstick    1.3.2 
v recipes      1.3.1      
-- Conflicts ----------------------------------------- tidymodels_conflicts() --
x purrr::discard()         masks scales::discard()
x dplyr::filter()          masks stats::filter()
x yardstick::get_weights() masks wpor::get_weights()
x dplyr::lag()             masks stats::lag()
x recipes::step()          masks stats::step()
Cross fitting treatment model
Cross fitting outcome model
Reshaping nuisance predictions
Fitting effect model
Cross fitting treatment model
Cross fitting outcome model
Reshaping nuisance predictions
Fitting effect model
Cross fitting treatment model
Cross fitting outcome model
Reshaping nuisance predictions
Cross fitting treatment model
Cross fitting outcome model
Reshaping nuisance predictions
Cross fitting treatment model
Cross fitting outcome model
Reshaping nuisance predictions
Cross fitting treatment model
Cross fitting outcome model
Reshaping nuisance predictions
Fitting effect model
Cross fitting treatment model
Cross fitting outcome model
Reshaping nuisance predictions
Fitting effect model
Tuning parameters using 2 folds, and 10 parameter sets.

Fold1: ..........v
Fold2: ..........v
Tuning parameters using 2 folds, and 10 parameter sets.

Fold1: ..........v
Fold2: ..........v
> A | warning: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide by 0 error. `NA` will be returned.
Tuning parameters using 3-4 folds (depending on tests after burn-in period), and 5 parameter sets.

Fold1(running 5/5 param sets): .....v
Fold2(running 5/5 param sets): .....v
Fold3(running 5/5 param sets): .....v
Fold4(running 5/5 param sets): .....v
Tuning parameters using 3-5 folds (depending on tests after burn-in period), and 5 parameter sets.

Fold1(running 5/5 param sets): .....v
Fold2(running 5/5 param sets): .....v
Fold3(running 5/5 param sets): .....v
Fold4(running 5/5 param sets): .....v
Fold5(running 4/5 param sets): ....v
[ FAIL 0 | WARN 78 | SKIP 0 | PASS 18 ]

[ FAIL 0 | WARN 78 | SKIP 0 | PASS 18 ]
> 
> proc.time()
   user  system elapsed 
  15.62    1.01   16.60