Package: CAST Check: re-building of vignette outputs New result: 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: • • • Report bugs at trying URL 'https://geodata.ucdavis.edu/climate/worldclim/2_1/base/wc2.1_10m_bio.zip' Content type 'application/zip' length 49869449 bytes (47.6 MB) ================================================== downloaded 47.6 MB trying URL 'https://geodata.ucdavis.edu/climate/worldclim/2_1/base/wc2.1_10m_elev.zip' Content type 'application/zip' length 1332437 bytes (1.3 MB) ================================================== downloaded 1.3 MB [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘cast01-CAST-intro.Rmd’ --- re-building ‘cast02-plotgeodist.Rmd’ using rmarkdown trying URL 'https://geodata.ucdavis.edu/climate/worldclim/2_1/base/wc2.1_10m_bio.zip' Content type 'application/zip' length 49869449 bytes (47.6 MB) ================================================== downloaded 47.6 MB [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘cast02-plotgeodist.Rmd’ --- re-building ‘cast03-CV.Rmd’ using rmarkdown Quitting from cast03-CV.Rmd:38-64 [read data] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: ! Cannot open "https://github.com/carlesmila/RF-spatial-proxies/raw/main/data/temp/temp_train.gpkg"; The file doesn't seem to exist. --- Backtrace: ▆ 1. └─sf::read_sf("https://github.com/carlesmila/RF-spatial-proxies/raw/main/data/temp/temp_train.gpkg") 2. ├─sf::st_read(...) 3. └─sf:::st_read.character(...) 4. └─sf:::CPL_read_ogr(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'cast03-CV.Rmd' failed with diagnostics: Cannot open "https://github.com/carlesmila/RF-spatial-proxies/raw/main/data/temp/temp_train.gpkg"; The file doesn't seem to exist. --- failed 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 file failed: ‘cast03-CV.Rmd’ Error: Vignette re-building failed. Execution halted Package: gdalcubes Check: whether package can be installed New result: ERROR Installation failed. Package: sf Check: tests New result: NOTE Running ‘aggregate.R’ [1s/1s] Comparing ‘aggregate.Rout’ to ‘aggregate.Rout.save’ ... OK Running ‘cast.R’ [1s/1s] Comparing ‘cast.Rout’ to ‘cast.Rout.save’ ... OK Running ‘crs.R’ [1s/1s] Comparing ‘crs.Rout’ to ‘crs.Rout.save’ ... OK Running ‘dist.R’ [1s/1s] Comparing ‘dist.Rout’ to ‘dist.Rout.save’ ... OK Running ‘dplyr.R’ [2s/2s] Comparing ‘dplyr.Rout’ to ‘dplyr.Rout.save’ ... OK Running ‘empty.R’ [1s/1s] Comparing ‘empty.Rout’ to ‘empty.Rout.save’ ... OK Running ‘full.R’ [1s/1s] Comparing ‘full.Rout’ to ‘full.Rout.save’ ... OK Running ‘gdal_geom.R’ [1s/1s] Comparing ‘gdal_geom.Rout’ to ‘gdal_geom.Rout.save’ ... OK Running ‘geos.R’ [15s/15s] Comparing ‘geos.Rout’ to ‘geos.Rout.save’ ... OK Running ‘graticule.R’ [2s/2s] Comparing ‘graticule.Rout’ to ‘graticule.Rout.save’ ... OK Running ‘grid.R’ [1s/1s] Comparing ‘grid.Rout’ to ‘grid.Rout.save’ ... OK Running ‘maps.R’ [2s/2s] Comparing ‘maps.Rout’ to ‘maps.Rout.save’ ... OK Running ‘plot.R’ [5s/5s] Comparing ‘plot.Rout’ to ‘plot.Rout.save’ ... OK Running ‘read.R’ [2s/2s] Comparing ‘read.Rout’ to ‘read.Rout.save’ ... OK Running ‘roundtrip.R’ [1s/1s] Comparing ‘roundtrip.Rout’ to ‘roundtrip.Rout.save’ ... OK Running ‘s2.R’ [1s/1s] Comparing ‘s2.Rout’ to ‘s2.Rout.save’ ... OK Running ‘sample.R’ [1s/1s] Comparing ‘sample.Rout’ to ‘sample.Rout.save’ ... OK Running ‘sfc.R’ [8s/8s] Comparing ‘sfc.Rout’ to ‘sfc.Rout.save’ ... OK Running ‘sfg.R’ [1s/1s] Comparing ‘sfg.Rout’ to ‘sfg.Rout.save’ ... OK Running ‘spatstat.R’ [2s/2s] Comparing ‘spatstat.Rout’ to ‘spatstat.Rout.save’ ... OK Running ‘stars.R’ [2s/2s] Comparing ‘stars.Rout’ to ‘stars.Rout.save’ ...302c302,317 < [1] "Cannot read blosc-compressed Zarr file: blosc not supported?" --- > stars object with 2 dimensions and 1 attribute > attribute(s): > Min. 1st Qu. Median Mean 3rd Qu. Max. > ones.zarr 1 1 1 1 1 1 > dimension(s): > from to offset delta x/y > x 1 100 0 1 [x] > y 1 100 100 -1 [y] > stars object with 2 dimensions and 1 attribute > attribute(s): > Min. 1st Qu. Median Mean 3rd Qu. Max. > ones 1 1 1 1 1 1 > dimension(s): > from to offset delta x/y > dim1 1 100 0.5 1 [x] > dim0 1 100 0.5 1 [y] Running ‘testthat.R’ [11s/12s] Running ‘units.R’ [1s/1s] Comparing ‘units.Rout’ to ‘units.Rout.save’ ... OK Running ‘wkb.R’ [1s/1s] Comparing ‘wkb.Rout’ to ‘wkb.Rout.save’ ... OK