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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/>


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<error/rlang_error>
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! Cannot open "https://github.com/carlesmila/RF-spatial-proxies/raw/main/data/AP/PM25_train.gpkg"; The file doesn't seem to exist.
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Backtrace:
    ▆
 1. └─sf::read_sf("https://github.com/carlesmila/RF-spatial-proxies/raw/main/data/AP/PM25_train.gpkg")
 2.   ├─sf::st_read(...)
 3.   └─sf:::st_read.character(...)
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