Last updated on 2026-01-20 07:50:48 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.3 | 17.85 | 646.38 | 664.23 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.3 | 13.33 | 440.26 | 453.59 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.0.3 | 30.00 | 1008.98 | 1038.98 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.3 | 32.00 | 1102.44 | 1134.44 | ERROR | |
| r-devel-windows-x86_64 | 1.0.3 | 19.00 | 606.00 | 625.00 | OK | |
| r-patched-linux-x86_64 | 1.0.3 | 17.28 | 625.92 | 643.20 | OK | |
| r-release-linux-x86_64 | 1.0.3 | 17.95 | 621.88 | 639.83 | OK | |
| r-release-macos-arm64 | 1.0.3 | OK | ||||
| r-release-macos-x86_64 | 1.0.3 | 10.00 | 685.00 | 695.00 | OK | |
| r-release-windows-x86_64 | 1.0.3 | 19.00 | 613.00 | 632.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.3 | OK | ||||
| r-oldrel-macos-x86_64 | 1.0.3 | 10.00 | 395.00 | 405.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.3 | 27.00 | 823.00 | 850.00 | OK |
Version: 1.0.3
Check: tests
Result: ERROR
Running ‘testthat.R’ [108s/134s]
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
Saving _problems/test-aoa-54.R
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
Saving _problems/test-aoa-71.R
Saving _problems/test-aoa-95.R
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
Saving _problems/test-aoa-119.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 0.953"
[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 0.954"
[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 0.954"
Spherical geometry (s2) switched off
Spherical geometry (s2) switched on
Spherical geometry (s2) switched off
Spherical geometry (s2) switched on
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched off
Spherical geometry (s2) switched on
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched off
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched on
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched off
variable(s) 'fct' is (are) treated as categorical variables
time variable that has been selected: Date
time variable that has been selected: Date
time variable that has been selected: Date
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 .
Spherical geometry (s2) switched on
although coordinates are longitude/latitude, st_sample assumes that they are
planar
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
although coordinates are longitude/latitude, st_sample assumes that they are
planar
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
although coordinates are longitude/latitude, st_sample assumes that they are
planar
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
1000 prediction points are sampled from the modeldomain
1000 prediction points are sampled from the modeldomain
1000 prediction points are sampled from the modeldomain
[ FAIL 4 | WARN 0 | SKIP 10 | PASS 109 ]
══ Skipped tests (10) ══════════════════════════════════════════════════════════
• On CRAN (10): 'test-errorProfiles.R:3:3', 'test-errorProfiles.R:36:3',
'test-errorProfiles.R:67:3', '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'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-aoa.R:51:3'): AOA works in default: used with raster data and a trained model ──
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.2858 " | "Mean :0.2858 " [4]
[5] "3rd Qu.:0.3815 " | "3rd Qu.:0.3815 " [5]
[6] "Max. :4.4485 " | "Max. :4.4485 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
── Failure ('test-aoa.R:68:3'): AOA works without a trained model ──────────────
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.3109 " | "Mean :0.3109 " [4]
[5] "3rd Qu.:0.4051 " | "3rd Qu.:0.4051 " [5]
[6] "Max. :2.6631 " | "Max. :2.6631 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
── Failure ('test-aoa.R:92:3'): AOA (including LPD) works with raster data and a trained model ──
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.2858 " | "Mean :0.2858 " [4]
[5] "3rd Qu.:0.3815 " | "3rd Qu.:0.3815 " [5]
[6] "Max. :4.4485 " | "Max. :4.4485 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
── Failure ('test-aoa.R:116:3'): AOA (inluding LPD) works without a trained model ──
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.3109 " | "Mean :0.3109 " [4]
[5] "3rd Qu.:0.4051 " | "3rd Qu.:0.4051 " [5]
[6] "Max. :2.6631 " | "Max. :2.6631 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
[ FAIL 4 | WARN 0 | SKIP 10 | PASS 109 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0.3
Check: tests
Result: ERROR
Running ‘testthat.R’ [270s/432s]
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
Saving _problems/test-aoa-54.R
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
Saving _problems/test-aoa-71.R
Saving _problems/test-aoa-95.R
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
Saving _problems/test-aoa-119.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 0.953"
[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 0.954"
[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 0.954"
Spherical geometry (s2) switched off
Spherical geometry (s2) switched on
Spherical geometry (s2) switched off
Spherical geometry (s2) switched on
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched off
Spherical geometry (s2) switched on
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched off
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched on
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched off
variable(s) 'fct' is (are) treated as categorical variables
time variable that has been selected: Date
time variable that has been selected: Date
time variable that has been selected: Date
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 .
Spherical geometry (s2) switched on
although coordinates are longitude/latitude, st_sample assumes that they are
planar
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
although coordinates are longitude/latitude, st_sample assumes that they are
planar
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
although coordinates are longitude/latitude, st_sample assumes that they are
planar
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
1000 prediction points are sampled from the modeldomain
1000 prediction points are sampled from the modeldomain
1000 prediction points are sampled from the modeldomain
[ FAIL 4 | WARN 0 | SKIP 10 | PASS 109 ]
══ Skipped tests (10) ══════════════════════════════════════════════════════════
• On CRAN (10): 'test-errorProfiles.R:3:3', 'test-errorProfiles.R:36:3',
'test-errorProfiles.R:67:3', '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'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-aoa.R:51:3'): AOA works in default: used with raster data and a trained model ──
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.2858 " | "Mean :0.2858 " [4]
[5] "3rd Qu.:0.3815 " | "3rd Qu.:0.3815 " [5]
[6] "Max. :4.4485 " | "Max. :4.4485 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
── Failure ('test-aoa.R:68:3'): AOA works without a trained model ──────────────
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.3109 " | "Mean :0.3109 " [4]
[5] "3rd Qu.:0.4051 " | "3rd Qu.:0.4051 " [5]
[6] "Max. :2.6631 " | "Max. :2.6631 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
── Failure ('test-aoa.R:92:3'): AOA (including LPD) works with raster data and a trained model ──
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.2858 " | "Mean :0.2858 " [4]
[5] "3rd Qu.:0.3815 " | "3rd Qu.:0.3815 " [5]
[6] "Max. :4.4485 " | "Max. :4.4485 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
── Failure ('test-aoa.R:116:3'): AOA (inluding LPD) works without a trained model ──
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.3109 " | "Mean :0.3109 " [4]
[5] "3rd Qu.:0.4051 " | "3rd Qu.:0.4051 " [5]
[6] "Max. :2.6631 " | "Max. :2.6631 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
[ FAIL 4 | WARN 0 | SKIP 10 | PASS 109 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.0.3
Check: tests
Result: ERROR
Running ‘testthat.R’ [5m/12m]
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
Saving _problems/test-aoa-54.R
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
Saving _problems/test-aoa-71.R
Saving _problems/test-aoa-95.R
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
Saving _problems/test-aoa-119.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 0.953"
[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 0.954"
[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 0.954"
Spherical geometry (s2) switched off
Spherical geometry (s2) switched on
Spherical geometry (s2) switched off
Spherical geometry (s2) switched on
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched off
Spherical geometry (s2) switched on
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched off
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched on
features are extracted from the modeldomain
samplesize for new data shouldn't be larger than number of pixels.
Samplesize was reduced to 100
Spherical geometry (s2) switched off
variable(s) 'fct' is (are) treated as categorical variables
time variable that has been selected: Date
time variable that has been selected: Date
time variable that has been selected: Date
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 .
Spherical geometry (s2) switched on
although coordinates are longitude/latitude, st_sample assumes that they are
planar
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
although coordinates are longitude/latitude, st_sample assumes that they are
planar
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
although coordinates are longitude/latitude, st_sample assumes that they are
planar
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
1000 prediction points are sampled from the modeldomain
1000 prediction points are sampled from the modeldomain
1000 prediction points are sampled from the modeldomain
[ FAIL 4 | WARN 0 | SKIP 10 | PASS 109 ]
══ Skipped tests (10) ══════════════════════════════════════════════════════════
• On CRAN (10): 'test-errorProfiles.R:3:3', 'test-errorProfiles.R:36:3',
'test-errorProfiles.R:67:3', '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'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-aoa.R:51:3'): AOA works in default: used with raster data and a trained model ──
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.2858 " | "Mean :0.2858 " [4]
[5] "3rd Qu.:0.3815 " | "3rd Qu.:0.3815 " [5]
[6] "Max. :4.4485 " | "Max. :4.4485 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
── Failure ('test-aoa.R:68:3'): AOA works without a trained model ──────────────
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.3109 " | "Mean :0.3109 " [4]
[5] "3rd Qu.:0.4051 " | "3rd Qu.:0.4051 " [5]
[6] "Max. :2.6631 " | "Max. :2.6631 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
── Failure ('test-aoa.R:92:3'): AOA (including LPD) works with raster data and a trained model ──
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.2858 " | "Mean :0.2858 " [4]
[5] "3rd Qu.:0.3815 " | "3rd Qu.:0.3815 " [5]
[6] "Max. :4.4485 " | "Max. :4.4485 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
── Failure ('test-aoa.R:116:3'): AOA (inluding LPD) works without a trained model ──
Expected `as.vector(summary(terra::values(AOA$DI)))` to equal `c(...)`.
Differences:
actual | expected
[4] "Mean :0.3109 " | "Mean :0.3109 " [4]
[5] "3rd Qu.:0.4051 " | "3rd Qu.:0.4051 " [5]
[6] "Max. :2.6631 " | "Max. :2.6631 " [6]
[7] "NAs :1993 " - "NA's :1993 " [7]
[ FAIL 4 | WARN 0 | SKIP 10 | PASS 109 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc