'caret' Applications for Spatial-Temporal Models

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. This package includes functions to improve spatial-temporal modelling tasks using 'caret'. It prepares data for Leave-Location-Out and Leave-Time-Out cross-validation which are target-oriented validation strategies for spatial-temporal models. To decrease overfitting and improve model performances, the package implements a forward feature selection that selects suitable predictor variables in view to their contribution to the target-oriented 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.

Caret Applications for Spatio-Temporal models

This is the developer version of CAST. The CRAN Version can be found on https://github.com/environmentalinformatics-marburg/CAST


CAST 0.3.1

  • bugfix:
    • CreateSpaceTimeFolds accepts tibbles
    • CreateSpaceTimeFolds automatically reduces k if necessary
    • ffs accepts further arguments taken by caret::train
  • new feature: plot_ffs has option to plot selected variables only

CAST 0.3.0

  • new feature: Best subset selection (bss) with target-oriented validation as (very slow but very reliable) alternative to ffs

  • minor adaptations: verbose option included, improved examples for ffs

  • bugfix: minor adaptations done for usage with plsr

CAST 0.2.1

  • new feature: Introduction to CAST is included as a vignette.

  • bugfix: minor error fixed in using user defined metrics for model selection.

CAST 0.2.0

  • bugfix: ffs with option withinSE=TRUE did not choose a model as "best model" if it was within the SE of a model that was trained in an earlier run but had the same number of variables. This bug is fixed and if withinSE=TRUE ffs now only compares the performance to models that use less variables (e.g. if a model using 5 variables is better than a model using 4 variables but still in the SE of the 4-variable model, then the 4-variable model is rated as the better model).

  • new feature: plot_ffs plots the results of ffs to visualize how the performance changes according to model run and the number of variables being used.

CAST 0.1.0

Initial public version on CRAN

Reference manual

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0.5.1 by Hanna Meyer, 8 months ago


Browse source code at https://github.com/cran/CAST

Authors: Hanna Meyer [cre, aut] , Chris Reudenbach [ctb] , Marvin Ludwig [ctb] , Thomas Nauss [ctb] , Edzer Pebesma [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports caret, stats, utils, ggplot2, graphics, reshape, FNN, plyr, zoo, methods, grDevices, data.table, lattice

Suggests doParallel, randomForest, lubridate, raster, sp, knitr, mapview, rmarkdown, sf, scales, parallel, latticeExtra, virtualspecies, gridExtra, viridis, rgeos, stars, scam

Suggested by mlr3spatiotempcv.

See at CRAN