CRAN release: 2023-11-07
CRAN release: 2023-11-03
spatial_block_cv()gains an argument,
expand_bbox, which represents the proportion a bounding box should be expanded by (each corner of the bounding box is expanded by
bbox_corner_value * expand_bbox).
- This is a breaking change for data in planar coordinate reference systems. Set to 0 to obtain previous behaviors.
- Data in geographic coordinates was already having its bounding box expanded by the default 0.00001.
- This makes it so that regularly spaced data is less likely to fall precisely along grid lines (and therefore fall into two assessment sets) and so that geographic data falls is more likely to fall within the constructed grid.
- Thanks to Nikos on StackOverflow for reporting this behavior: https://stackoverflow.com/q/77374348/9625040
spatial_block_cv()will now throw an error if observations are in multiple assessment folds (caused by observations, or observation centroids, falling precisely along grid polygon boundaries).
spatial_nndm_cv(), passing a single polygon (or multipolygon) to the
prediction_sitesargument will result in prediction sites being sampled from that polygon, rather than from its bounding box.
get_rsplit()is now re-exported from the rsample package. This provides a more natural, pipe-able interface for accessing individual splits;
get_rsplit(rset, 1)is identical to
CRAN release: 2023-05-17
CRAN release: 2023-01-17
CRAN release: 2022-08-05
Mike Mahoney is taking over as package maintainer, as Julia Silge (who remains a package author) moves to focus on ModelOps work.
Functions will now return rsplits without
out_id, like most rsample functions, whenever
spatial_buffer_vfold_cv(), and buffering now support using sf or sfc objects with a missing CRS. The assumption is that data in an NA CRS is projected, with all distance values in the same unit as the projection. Trying to use alternative units will fail. Set a CRS if these assumptions aren’t correct.
spatial_buffer_vfold_cv()and buffering no longer support tibble or data.frame inputs (they now require sf or sfc objects). It was not easy to use these to begin with, but should have always caused an error: use
rsample::vfold_cv()instead or transform your data into an sf object.
spatial_buffer_vfold_cv()has had some attribute changes to match
strataattribute is now the name of the column used for stratification, or not set if there was no stratification.
breakshave been added as attributes
bufferare now set to 0 if they were passed as
CRAN release: 2022-06-17
spatial_buffer_vfold_cv()is a new function which wraps
rsample::vfold_cv(), allowing users to add inclusion radii and exclusion buffers to their vfold resamples. This is the supported way to perform spatially buffered leave-one-out cross validation (set
spatial_block_cv()is a new function for performing spatial block cross-validation. It currently supports randomly assigning blocks to folds.
spatial_clustering_cv()gains an argument,
cluster_function, which specifies what type of clustering to perform.
cluster_function = "kmeans", the default, uses
stats::kmeans()for k-means clustering, while
cluster_function = "hclust"uses
stats::hclust()for hierarchical clustering. Users can also provide their own clustering function.
sfobjects! Coordinates are inferred automatically when using
sfobjects, and anything passed to
coordswill be ignored with a warning. Clusters made using
sfobjects will take coordinate reference systems into account (using
sf::st_distance()), unlike those made using data frames.
All resampling functions now support spatial buffering using two arguments.
radiuslets you specify an inclusion radius for your test set, where any data within
radiusof the original assessment set will be added to the assessment set.
bufferspecifies an exclusion buffer around the test set, where any data within
bufferof the assessment set (after
radiusis applied) will be excluded from both sets.
boston_canopyis a new dataset with data on tree canopy change over time in Boston, Massachusetts, USA. It uses a projected coordinate reference system and US customary units; see
?boston_canopyfor instructions on how to install these into your PROJ installation if needed.
The “Getting Started” vignette has been revised to demonstrate the new features and clustering methods.
A new vignette has been added walking through the spatial buffering process.