Last updated on 2026-06-20 00:49:26 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.4 | 20.02 | 624.28 | 644.30 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.4 | 11.72 | 433.33 | 445.05 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.4 | 33.00 | 953.49 | 986.49 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.4 | 30.00 | 904.87 | 934.87 | ERROR | |
| r-devel-windows-x86_64 | 1.0.4 | 22.00 | 598.00 | 620.00 | OK | |
| r-patched-linux-x86_64 | 1.0.4 | 18.90 | 511.95 | 530.85 | ERROR | |
| r-release-linux-x86_64 | 1.0.4 | 16.66 | 621.76 | 638.42 | OK | |
| r-release-macos-arm64 | 1.1.0 | 4.00 | 198.00 | 202.00 | ERROR | |
| r-release-macos-x86_64 | 1.1.0 | 13.00 | 560.00 | 573.00 | OK | |
| r-release-windows-x86_64 | 1.0.4 | 20.00 | 1515.00 | 1535.00 | OK | |
| r-oldrel-macos-arm64 | 1.1.0 | 4.00 | 217.00 | 221.00 | OK | |
| r-oldrel-macos-x86_64 | 1.1.0 | 12.00 | 591.00 | 603.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.4 | 28.00 | 786.00 | 814.00 | OK |
Version: 1.0.4
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘cast01-CAST-intro.Rmd’ using rmarkdown
CAST package:CAST R Documentation
'_<08>c_<08>a_<08>r_<08>e_<08>t' _<08>A_<08>p_<08>p_<08>l_<08>i_<08>c_<08>a_<08>t_<08>i_<08>o_<08>n_<08>s _<08>f_<08>o_<08>r _<08>S_<08>p_<08>a_<08>t_<08>i_<08>a_<08>l-_<08>T_<08>e_<08>m_<08>p_<08>o_<08>r_<08>a_<08>l _<08>M_<08>o_<08>d_<08>e_<08>l_<08>s
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
Supporting functionality to run 'caret' with spatial or
spatial-temporal data. 'caret' is a frequently used package for
model training and prediction using machine learning. CAST
includes functions to improve spatial-temporal modelling tasks
using 'caret'. It includes the newly suggested 'Nearest neighbor
distance matching' cross-validation to estimate the performance of
spatial prediction models and allows for spatial variable
selection to selects suitable predictor variables in view to their
contribution to the spatial model performance. CAST further
includes functionality to estimate the (spatial) area of
applicability of prediction models by analysing the similarity
between new data and training data. Methods are described in Meyer
et al. (2018); Meyer et al. (2019); Meyer and Pebesma (2021); Milà
et al. (2022); Meyer and Pebesma (2022); Linnenbrink et al.
(2023). The package is described in detail in Meyer et al. (2024).
_<08>D_<08>e_<08>t_<08>a_<08>i_<08>l_<08>s:
'caret' Applications for Spatio-Temporal models
_<08>A_<08>u_<08>t_<08>h_<08>o_<08>r(_<08>s):
Hanna Meyer, Carles Milà, Marvin Ludwig, Jan Linnenbrink, Fabian
Schumacher
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
• Meyer, H., Ludwig, L., Milà, C., Linnenbrink, J., Schumacher,
F. (2026): The CAST package for training and assessment of
spatial prediction models in R. In: Rocchini, D. (eds) R
Coding for Ecology. Use R!. Springer, Cham.
• Schumacher, F., Knoth, C., Ludwig, M., Meyer, H. (2025):
Estimation of local training data point densities to support
the assessment of spatial prediction uncertainty. Geosci.
Model Dev., 18, 10185–10202.
• Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H. (2024):
kNNDM: k-fold Nearest Neighbour Distance Matching
Cross-Validation for map accuracy estimation, Geosci. Model
Dev., 17, 5897–5912.
• Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest
Neighbour Distance Matching Leave-One-Out Cross-Validation
for map validation. Methods in Ecology and Evolution 13,
1304– 1316.
• Meyer, H., Pebesma, E. (2022): Machine learning-based global
maps of ecological variables and the challenge of assessing
them. Nature Communications. 13.
• Meyer, H., Pebesma, E. (2021): Predicting into unknown space?
Estimating the area of applicability of spatial prediction
models. Methods in Ecology and Evolution. 12, 1620– 1633.
• Meyer, H., Reudenbach, C., Wöllauer, S., Nauss, T. (2019):
Importance of spatial predictor variable selection in machine
learning applications - Moving from data reproduction to
spatial prediction. Ecological Modelling. 411, 108815.
• Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauß, T.
(2018): Improving performance of spatio-temporal machine
learning models using forward feature selection and
target-oriented validation. Environmental Modelling &
Software 101: 1-9.
_<08>S_<08>e_<08>e _<08>A_<08>l_<08>s_<08>o:
Useful links:
• <https://github.com/HannaMeyer/CAST>
• <https://hannameyer.github.io/CAST/>
• Report bugs at <https://github.com/HannaMeyer/CAST/issues/>
Quitting from cast01-CAST-intro.Rmd:72-78 [unnamed-chunk-5]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unable to find an inherited method for function 'crop' for signature 'x = "NULL"'
---
Backtrace:
▆
1. └─terra::crop(c(wc, elev), st_bbox(splotdata))
2. └─methods (local) `<fn>`(`<list>`, `<stndrdGn>`, `<env>`)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'cast01-CAST-intro.Rmd' failed with diagnostics:
unable to find an inherited method for function 'crop' for signature 'x = "NULL"'
--- failed re-building ‘cast01-CAST-intro.Rmd’
--- re-building ‘cast02-plotgeodist.Rmd’ using rmarkdown
Quitting from cast02-plotgeodist.Rmd:243-248 [unnamed-chunk-17]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `names(predictors_global) <- c(paste0("bio_", 1:19))`:
! attempt to set an attribute on NULL
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'cast02-plotgeodist.Rmd' failed with diagnostics:
attempt to set an attribute on NULL
--- failed re-building ‘cast02-plotgeodist.Rmd’
--- re-building ‘cast03-CV.Rmd’ using rmarkdown
--- finished re-building ‘cast03-CV.Rmd’
--- re-building ‘cast04-AOA-tutorial.Rmd’ using rmarkdown
--- finished re-building ‘cast04-AOA-tutorial.Rmd’
--- re-building ‘cast05-parallel.Rmd’ using rmarkdown
--- finished re-building ‘cast05-parallel.Rmd’
SUMMARY: processing the following files failed:
‘cast01-CAST-intro.Rmd’ ‘cast02-plotgeodist.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 1.0.4
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘cast01-CAST-intro.Rmd’ using rmarkdown
CAST package:CAST R Documentation
'_<08>c_<08>a_<08>r_<08>e_<08>t' _<08>A_<08>p_<08>p_<08>l_<08>i_<08>c_<08>a_<08>t_<08>i_<08>o_<08>n_<08>s _<08>f_<08>o_<08>r _<08>S_<08>p_<08>a_<08>t_<08>i_<08>a_<08>l-_<08>T_<08>e_<08>m_<08>p_<08>o_<08>r_<08>a_<08>l _<08>M_<08>o_<08>d_<08>e_<08>l_<08>s
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
Supporting functionality to run 'caret' with spatial or
spatial-temporal data. 'caret' is a frequently used package for
model training and prediction using machine learning. CAST
includes functions to improve spatial-temporal modelling tasks
using 'caret'. It includes the newly suggested 'Nearest neighbor
distance matching' cross-validation to estimate the performance of
spatial prediction models and allows for spatial variable
selection to selects suitable predictor variables in view to their
contribution to the spatial model performance. CAST further
includes functionality to estimate the (spatial) area of
applicability of prediction models by analysing the similarity
between new data and training data. Methods are described in Meyer
et al. (2018); Meyer et al. (2019); Meyer and Pebesma (2021); Milà
et al. (2022); Meyer and Pebesma (2022); Linnenbrink et al.
(2023). The package is described in detail in Meyer et al. (2024).
_<08>D_<08>e_<08>t_<08>a_<08>i_<08>l_<08>s:
'caret' Applications for Spatio-Temporal models
_<08>A_<08>u_<08>t_<08>h_<08>o_<08>r(_<08>s):
Hanna Meyer, Carles Milà, Marvin Ludwig, Jan Linnenbrink, Fabian
Schumacher
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
• Meyer, H., Ludwig, L., Milà, C., Linnenbrink, J., Schumacher,
F. (2026): The CAST package for training and assessment of
spatial prediction models in R. In: Rocchini, D. (eds) R
Coding for Ecology. Use R!. Springer, Cham.
• Schumacher, F., Knoth, C., Ludwig, M., Meyer, H. (2025):
Estimation of local training data point densities to support
the assessment of spatial prediction uncertainty. Geosci.
Model Dev., 18, 10185–10202.
• Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H. (2024):
kNNDM: k-fold Nearest Neighbour Distance Matching
Cross-Validation for map accuracy estimation, Geosci. Model
Dev., 17, 5897–5912.
• Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest
Neighbour Distance Matching Leave-One-Out Cross-Validation
for map validation. Methods in Ecology and Evolution 13,
1304– 1316.
• Meyer, H., Pebesma, E. (2022): Machine learning-based global
maps of ecological variables and the challenge of assessing
them. Nature Communications. 13.
• Meyer, H., Pebesma, E. (2021): Predicting into unknown space?
Estimating the area of applicability of spatial prediction
models. Methods in Ecology and Evolution. 12, 1620– 1633.
• Meyer, H., Reudenbach, C., Wöllauer, S., Nauss, T. (2019):
Importance of spatial predictor variable selection in machine
learning applications - Moving from data reproduction to
spatial prediction. Ecological Modelling. 411, 108815.
• Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauß, T.
(2018): Improving performance of spatio-temporal machine
learning models using forward feature selection and
target-oriented validation. Environmental Modelling &
Software 101: 1-9.
_<08>S_<08>e_<08>e _<08>A_<08>l_<08>s_<08>o:
Useful links:
• <https://github.com/HannaMeyer/CAST>
• <https://hannameyer.github.io/CAST/>
• Report bugs at <https://github.com/HannaMeyer/CAST/issues/>
Quitting from cast01-CAST-intro.Rmd:72-78 [unnamed-chunk-5]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unable to find an inherited method for function 'crop' for signature 'x = "NULL"'
---
Backtrace:
▆
1. └─terra::crop(c(wc, elev), st_bbox(splotdata))
2. └─methods (local) `<fn>`(`<list>`, `<stndrdGn>`, `<env>`)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'cast01-CAST-intro.Rmd' failed with diagnostics:
unable to find an inherited method for function 'crop' for signature 'x = "NULL"'
--- failed re-building ‘cast01-CAST-intro.Rmd’
--- re-building ‘cast02-plotgeodist.Rmd’ using rmarkdown
Quitting from cast02-plotgeodist.Rmd:243-248 [unnamed-chunk-17]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `names(predictors_global) <- c(paste0("bio_", 1:19))`:
! attempt to set an attribute on NULL
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'cast02-plotgeodist.Rmd' failed with diagnostics:
attempt to set an attribute on NULL
--- failed re-building ‘cast02-plotgeodist.Rmd’
--- re-building ‘cast03-CV.Rmd’ using rmarkdown
--- finished re-building ‘cast03-CV.Rmd’
--- re-building ‘cast04-AOA-tutorial.Rmd’ using rmarkdown
--- finished re-building ‘cast04-AOA-tutorial.Rmd’
--- re-building ‘cast05-parallel.Rmd’ using rmarkdown
--- finished re-building ‘cast05-parallel.Rmd’
SUMMARY: processing the following files failed:
‘cast01-CAST-intro.Rmd’ ‘cast02-plotgeodist.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-patched-linux-x86_64
Version: 1.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [60s/61s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(CAST)
>
> test_check("CAST")
Loading required package: ggplot2
Loading required package: lattice
note: variables were not weighted either because no weights or model were given,
no variable importance could be retrieved from the given model, or the model has a single feature.
Check caret::varImp(model)
note: No model and no CV folds were given. The DI threshold is therefore based on all training data
note: variables were not weighted either because no weights or model were given,
no variable importance could be retrieved from the given model, or the model has a single feature.
Check caret::varImp(model)
note: No model and no CV folds were given. The DI threshold is therefore based on all training data
DI:
class : SpatRaster
size : 58, 101, 1 (nrow, ncol, nlyr)
resolution : 10, 10 (x, y)
extent : 493179, 494189, 5180552, 5181132 (xmin, xmax, ymin, ymax)
coord. ref. : NAD83 / UTM zone 11N (EPSG:26911)
source(s) : memory
varname : predictors_2012-03-25
name : DI
min value : 0
max value : 4.448467
AOA:
class : SpatRaster
size : 58, 101, 1 (nrow, ncol, nlyr)
resolution : 10, 10 (x, y)
extent : 493179, 494189, 5180552, 5181132 (xmin, xmax, ymin, ymax)
coord. ref. : NAD83 / UTM zone 11N (EPSG:26911)
source(s) : memory
varname : predictors_2012-03-25
name : AOA
min value : 0
max value : 1
Predictor Weights:
DEM TWI NDRE.Sd
1 1.279708 8.469273 4.22456
AOA Threshold: 0.3898564DI:
class : SpatRaster
size : 58, 101, 1 (nrow, ncol, nlyr)
resolution : 10, 10 (x, y)
extent : 493179, 494189, 5180552, 5181132 (xmin, xmax, ymin, ymax)
coord. ref. : NAD83 / UTM zone 11N (EPSG:26911)
source(s) : memory
varname : predictors_2012-03-25
name : DI
min value : 0
max value : 4.448467
AOA:
class : SpatRaster
size : 58, 101, 1 (nrow, ncol, nlyr)
resolution : 10, 10 (x, y)
extent : 493179, 494189, 5180552, 5181132 (xmin, xmax, ymin, ymax)
coord. ref. : NAD83 / UTM zone 11N (EPSG:26911)
source(s) : memory
varname : predictors_2012-03-25
name : AOA
min value : 0
max value : 1
Predictor Weights:
DEM TWI NDRE.Sd
1 1.279708 8.469273 4.22456
AOA Threshold: 0.3898564Saving _problems/test-bss-14.R
Saving _problems/test-bss-17.R
[1] "model using Sepal.Length,Sepal.Width will be trained now..."
note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
[1] "maximum number of models that still need to be trained: 8"
[1] "model using Sepal.Length,Petal.Length will be trained now..."
note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
[1] "maximum number of models that still need to be trained: 7"
[1] "model using Sepal.Length,Petal.Width will be trained now..."
note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
[1] "maximum number of models that still need to be trained: 6"
[1] "model using Sepal.Width,Petal.Length will be trained now..."
note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
[1] "maximum number of models that still need to be trained: 5"
[1] "model using Sepal.Width,Petal.Width will be trained now..."
note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
[1] "maximum number of models that still need to be trained: 4"
[1] "model using Petal.Length,Petal.Width will be trained now..."
note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
[1] "maximum number of models that still need to be trained: 3"
[1] "vars selected: Petal.Length,Petal.Width with Accuracy 0.954"
[1] "model using additional variable Sepal.Length will be trained now..."
note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 .
[1] "maximum number of models that still need to be trained: 2"
[1] "model using additional variable Sepal.Width will be trained now..."
note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 .
[1] "maximum number of models that still need to be trained: 1"
[1] "vars selected: Petal.Length,Petal.Width,Sepal.Width with Accuracy 0.955"
[1] "model using additional variable Sepal.Length will be trained now..."
[1] "maximum number of models that still need to be trained: 0"
[1] "vars selected: Petal.Length,Petal.Width,Sepal.Width with Accuracy 0.955"
Sampling 2000 prediction locations from the modeldomain raster.
Sampling 2000 prediction locations from the modeldomain vector.
Sampling 2000 prediction locations from the modeldomain vector.
Sampling 2000 prediction locations from the modeldomain raster.
Sampling 2000 prediction locations from the modeldomain raster.
Extracting predictors from modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Sampling 100 prediction locations from the modeldomain raster.
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Sampling 100 prediction locations from the modeldomain raster.
Extracting predictors from modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Sampling 100 prediction locations from the modeldomain raster.
Extracting predictors from modeldomain
Sampling 2000 prediction locations from the modeldomain vector.
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Sampling 100 prediction locations from the modeldomain raster.
Extracting predictors from modeldomain
Sampling 2000 prediction locations from the modeldomain raster.
time variable selected: Date
time variable selected: Date
time variable selected: Date
Sampling 2000 prediction locations from the modeldomain raster.
note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 .
note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 .
although coordinates are longitude/latitude, st_sample assumes that they are
planar
1000 prediction points are sampled from the modeldomain
Gij <= Gj; a random CV assignment is returned
1000 prediction points are sampled from the modeldomain
Gij <= Gj; a random CV assignment is returned
1000 prediction points are sampled from the modeldomain
predictor values are extracted for prediction points
Gij <= Gj; a random CV assignment is returned
1000 prediction points are sampled from the modeldomain
predictor values are extracted for prediction points
Gij <= Gj; a random CV assignment is returned
1000 prediction points are sampled from the modeldomain
predictor values are extracted for prediction points
variable(s) 'fct' is (are) treated as categorical variables
some prediction points contain NAs, which will be removed
Gij <= Gj; a random CV assignment is returned
1000 prediction points are sampled from the modeldomain
predictor values are extracted for prediction points
variable(s) 'fct' is (are) treated as categorical variables
1000 prediction points are sampled from the modeldomain
predictor values are extracted for prediction points
variable(s) 'fct' is (are) treated as categorical variables
1000 prediction points are sampled from the modeldomain
predictor values are extracted for prediction points
1000 prediction points are sampled from the modeldomain
predictor values are extracted for prediction points
1000 prediction points are sampled from the modeldomain
predictor values are extracted for prediction points
1000 prediction points are sampled from the modeldomain
predictor values are extracted for prediction points
knndm object
Space:
Clustering algorithm: kmeans
Intermediate clusters (q): 4
W statistic: 1.065
Number of folds: 2
Observations in each fold: 4 2
knndm object
Space:
Clustering algorithm: kmeans
Intermediate clusters (q): 4
W statistic: 1.065
Number of folds: 2
Observations in each fold: 4 2
1000 prediction points are sampled from the modeldomain
1000 prediction points are sampled from the modeldomain
1000 prediction points are sampled from the modeldomain
1000 prediction points are sampled from the modeldomain
1000 prediction points are sampled from the modeldomain
predictor values are extracted for prediction points
1000 prediction points are sampled from the modeldomain
nndm object
Total number of points: 10
Mean number of training points: 8.8
Minimum number of training points: 8
nndm object
Total number of points: 10
Mean number of training points: 8.8
Minimum number of training points: 8
No trainDI provided.
Computing DI of training data...
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Computing DI of new data...
Computing AOA...
Finished!
DI of 30 observation
Predictors: DEM TWI NDRE.Sd
AOA Threshold: 0.3898564DI of 30 observation
Predictors: DEM TWI NDRE.Sd
AOA Threshold: 0.3898564[ FAIL 2 | WARN 0 | SKIP 20 | PASS 144 ]
══ Skipped tests (20) ══════════════════════════════════════════════════════════
• On CRAN (20): 'test-aoa.R:124:3', 'test-aoa.R:150:3', 'test-aoa.R:184:3',
'test-aoa.R:197:3', 'test-errorProfiles.R:3:3', 'test-errorProfiles.R:36:3',
'test-errorProfiles.R:67:3', 'test-errorProfiles.R:95:3',
'test-errorProfiles.R:119:3', 'test-fss.R:3:3', 'test-fss.R:27:3',
'test-fss.R:47:3', 'test-fss.R:70:3', 'test-fss.R:120:5', 'test-fss.R:135:5',
'test-fss.R:152:5', 'test-fss.R:171:5', 'test-fss.R:192:5',
'test-fss.R:210:5', 'test-fss.R:232:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-bss.R:14:3'): bss works with default arguments ───────────────
Expected `selection$selectedvars` to be identical to `c("bio_1", "bio_4", "bio_5", "bio_6", "bio_8", "bio_12")`.
Differences:
`actual`: "bio_1" "bio_4" "bio_6" "bio_12"
`expected`: "bio_1" "bio_4" "bio_5" "bio_6" "bio_8" "bio_12"
── Failure ('test-bss.R:17:3'): bss works with default arguments ───────────────
Expected `round(selection$results$RMSE, 2)` to be identical to 27.4.
Differences:
`actual`: 27.69
`expected`: 27.40
[ FAIL 2 | WARN 0 | SKIP 20 | PASS 144 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-macos-arm64