• using R Under development (unstable) (2025-11-24 r89053)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2)     GNU Fortran (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2)
• running under: Fedora Linux 42 (Workstation Edition)
• using session charset: UTF-8
• using option ‘--no-stop-on-test-error’
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• checking extension type ... Package
• this is package ‘CAST’ version ‘1.0.3’
• package encoding: UTF-8
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• checking re-building of vignette outputs ... [6m/14m] ERROR Error(s) in re-building vignettes: --- re-building ‘cast01-CAST-intro.Rmd’ using rmarkdown CAST package:CAST R Documentation '_ c_ a_ r_ e_ t' _ A_ p_ p_ l_ i_ c_ a_ t_ i_ o_ n_ s _ f_ o_ r _ S_ p_ a_ t_ i_ a_ l-_ T_ e_ m_ p_ o_ r_ a_ l _ M_ o_ d_ e_ l_ s _ D_ e_ s_ c_ r_ i_ p_ t_ i_ o_ 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). _ D_ e_ t_ a_ i_ l_ s:      'caret' Applications for Spatio-Temporal models _ A_ u_ t_ h_ o_ r(_ s):      Hanna Meyer, Carles Milà, Marvin Ludwig, Jan Linnenbrink, Fabian      Schumacher _ R_ e_ f_ e_ r_ e_ n_ c_ e_ s:         • Meyer, H., Ludwig, L., Milà, C., Linnenbrink, J., Schumacher,           F. (2024): The CAST package for training and assessment of           spatial prediction models in R. arXiv,           https://doi.org/10.48550/arXiv.2404.06978.         • Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM:           k-fold Nearest Neighbour Distance Matching Cross-Validation           for map accuracy estimation, EGUsphere [preprint],           https://doi.org/10.5194/egusphere-2023-1308, 2023.         • 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 00, 1–           13.         • 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. _ S_ e_ e _ A_ l_ s_ 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:51-57 [unnamed-chunk-3] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <error/rlang_error> Error in `library()`: ! there is no package called 'tmap' --- Backtrace:     ▆  1. └─base::library(tmap) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'cast01-CAST-intro.Rmd' failed with diagnostics: there is no package called 'tmap' --- 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 [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘cast03-CV.Rmd’ --- re-building ‘cast04-AOA-tutorial.Rmd’ using rmarkdown [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘cast04-AOA-tutorial.Rmd’ --- re-building ‘cast05-parallel.Rmd’ using rmarkdown [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- 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
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• DONE

Status: 1 ERROR