CRAN Package Check Results for Package CAST

Last updated on 2023-01-28 07:14:50 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 15.57 0.02 15.59 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 20.41 1512.45 1532.86 ERROR
r-release-linux-x86_64 0.7.0 11.99 1349.39 1361.38 ERROR
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 32.00 0.00 32.00 FAIL
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 0.00 40.00 FAIL

Check Details

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
Flavors: r-devel-linux-x86_64-debian-clang, r-patched-linux-x86_64

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

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
Flavor: r-release-linux-x86_64

Version: 0.7.0
Check: re-building of vignette outputs
Result: FAIL
    Check process probably crashed or hung up for 20 minutes ... killed
    Most likely this happened in the example checks (?),
    if not, ignore the following last lines of example output:
    > ##D plot(varImp(model,scale=FALSE))
    > ##D
    > ##D #...then calculate the DI of the trained model:
    > ##D DI = trainDI(model=model)
    > ##D plot(DI)
    > ##D
    > ##D # the DI can now be used to compute the AOA:
    > ##D AOA = aoa(studyArea, model = model, trainDI = DI)
    > ##D print(AOA)
    > ##D plot(AOA)
    > ## End(Not run)
    >
    >
    >
    >
    > ### * <FOOTER>
    > ###
    > cleanEx()
    > options(digits = 7L)
    > base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
    Time elapsed: 16.31 0.79 17.2 NA NA
    > grDevices::dev.off()
    null device
     1
    > ###
    > ### Local variables: ***
    > ### mode: outline-minor ***
    > ### outline-regexp: "\\(> \\)?### [*]+" ***
    > ### End: ***
    > quit('no')
    ======== End of example output (where/before crash/hang up occured ?) ========
Flavor: r-release-windows-x86_64

Version: 0.7.0
Check: re-building of vignette outputs
Result: FAIL
    Check process probably crashed or hung up for 20 minutes ... killed
    Most likely this happened in the example checks (?),
    if not, ignore the following last lines of example output:
    > ##D plot(varImp(model,scale=FALSE))
    > ##D
    > ##D #...then calculate the DI of the trained model:
    > ##D DI = trainDI(model=model)
    > ##D plot(DI)
    > ##D
    > ##D # the DI can now be used to compute the AOA:
    > ##D AOA = aoa(studyArea, model = model, trainDI = DI)
    > ##D print(AOA)
    > ##D plot(AOA)
    > ## End(Not run)
    >
    >
    >
    >
    > ### * <FOOTER>
    > ###
    > cleanEx()
    > options(digits = 7L)
    > base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
    Time elapsed: 15.09 1.01 16.15 NA NA
    > grDevices::dev.off()
    null device
     1
    > ###
    > ### Local variables: ***
    > ### mode: outline-minor ***
    > ### outline-regexp: "\\(> \\)?### [*]+" ***
    > ### End: ***
    > quit('no')
    ======== End of example output (where/before crash/hang up occured ?) ========
Flavor: r-oldrel-windows-ix86+x86_64