Last updated on 2023-01-25 04:51:32 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.7.0 | 20.49 | 1542.06 | 1562.55 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.7.0 | 15.41 | 0.01 | 15.42 | FAIL | |
| r-devel-linux-x86_64-fedora-clang | 0.7.0 | 1982.68 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.7.0 | 1725.41 | ERROR | |||
| r-devel-windows-x86_64 | 0.7.0 | 33.00 | 511.00 | 544.00 | OK | |
| r-patched-linux-x86_64 | 0.7.0 | 22.64 | 467.06 | 489.70 | OK | |
| r-release-linux-x86_64 | 0.7.0 | 11.97 | 403.53 | 415.50 | OK | |
| r-release-macos-arm64 | 0.7.0 | 139.00 | NOTE | |||
| r-release-macos-x86_64 | 0.7.0 | 204.00 | NOTE | |||
| r-release-windows-x86_64 | 0.7.0 | 28.00 | 443.00 | 471.00 | OK | |
| r-oldrel-macos-arm64 | 0.7.0 | 131.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 0.7.0 | 224.00 | NOTE | |||
| r-oldrel-windows-ix86+x86_64 | 0.7.0 | 40.00 | 540.00 | 580.00 | OK |
Version: 0.7.0
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).
_<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
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
• 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.
--- finished re-building ‘cast01-CAST-intro.Rmd’
--- re-building ‘cast02-AOA-tutorial.Rmd’ using rmarkdown
--- finished re-building ‘cast02-AOA-tutorial.Rmd’
--- re-building ‘cast03-AOA-parallel.Rmd’ using rmarkdown
--- finished re-building ‘cast03-AOA-parallel.Rmd’
--- re-building ‘cast04-plotgeodist.Rmd’ using rmarkdown
Killed
SUMMARY: processing the following file failed:
‘cast04-plotgeodist.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.7.0
Check: re-building of vignette outputs
Result: FAIL
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.7.0
Check: dependencies in R code
Result: NOTE
Namespace in Imports field not imported from: ‘reshape’
All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64
Version: 0.7.0
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).
_<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
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
• 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.
--- finished re-building ‘cast01-CAST-intro.Rmd’
--- re-building ‘cast02-AOA-tutorial.Rmd’ using rmarkdown
--- finished re-building ‘cast02-AOA-tutorial.Rmd’
--- re-building ‘cast03-AOA-parallel.Rmd’ using rmarkdown
--- finished re-building ‘cast03-AOA-parallel.Rmd’
Warning: elapsed-time limit of 30 minutes reached for sub-process
--- re-building ‘cast04-plotgeodist.Rmd’ using rmarkdown
Execution halted
SUMMARY: processing the following file failed:
‘cast04-plotgeodist.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc