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SpatialML 1.8.2 (2026-06-10)

Packaging

• Added the .Rbuildignore file so that top-level development artefacts (the RStudio project file, the technical notes document, local tool settings and cran-comments.md) are excluded from the source tarball. The 1.8.1 release notes stated this exclusion was already in place, but the file itself was missing; R CMD check --as-cran consequently reported NOTEs about hidden and non-standard top-level files. Both NOTEs are now resolved.
• Added cran-comments.md (not shipped in the tarball) documenting the test environments and the resubmission context.

DESCRIPTION metadata

• Added the maintainer’s ORCID iD to Authors@R.
• Simplified the roles of Stefanos Georganos to "aut" (the previous c("aut", "ctb") was redundant).
• Added Language: en-GB, matching the British spelling used throughout the documentation.

Documentation

• The \donttest{} examples of grf() and predict.grf() now set nthreads = 1 explicitly. This was already CRAN-compliant (ranger defaults to two threads) and is purely a defensive clarification.

SpatialML 1.8.1

CRAN Resubmission

• Prepared the package for CRAN resubmission after archival. The known CRAN archival issue was an Rd cross-reference NOTE for unanchored external links to ranger; all such references now use \link[ranger]{ranger}.
• Version and date updated to 1.8.1 / 2026-06-09.
• Top-level development artefacts are excluded from R CMD build through .Rbuildignore.
• Removed “active development” wording from help pages to better match CRAN’s publication-quality expectation.

Dependency Reduction

• The package now depends only on R in Depends: and imports ranger, stats, graphics and utils.
• Removed the runtime dependencies on caret and randomForest. The two affected functions, rf.mtry.optim() and grf.bw(), are now implemented directly with ranger and base R helpers.
• Replaced broad namespace exports/imports with explicit export(), S3method() and importFrom() declarations.

grf() Robustness

• grf() no longer repeatedly refits local forests when ranger returns a NaN out-of-bag prediction for the focal observation. The default fallback is now a leave-one-out local refit: the focal row is removed from the local training subset and predicted out-of-sample. This preserves finite local diagnostics without noisy console output.
• Added oob.fallback = c("loo", "inbag") to grf(). The default "loo" uses the new leave-one-out fallback; "inbag" reproduces the older in-sample fallback behaviour.
• The focal observation is now forced to be the first row of each local subset before reading ranger’s first training prediction. This avoids incorrect focal OOB extraction when two or more observations share the same coordinates.
• Degenerate zero-distance local kernels now receive equal weights instead of producing non-finite bi-square weights.
• The local-fit loop no longer rebuilds a full copy of dframe on every iteration. Local neighbourhoods are indexed by row position, reducing memory traffic for medium-sized data sets.
• Added validation for coords, bw, ntree, kernel and formula inputs, plus clearer fixed-bandwidth diagnostics when too few neighbours are available.
• grf() now returns an object of class "grf" so predict() dispatches to predict.grf().
• LocalModelSummary now reports AIC and AICc for both OOB and predicted residuals.

Bandwidth and mtry Selection

• grf.bw() no longer uses caret::postResample(). Its Mixed and Low.Local diagnostics are computed by an internal base R helper equivalent to squared correlation.
• grf.bw() returns Best.BW = NA_real_ with an informative warning when every evaluated bandwidth produces a non-finite local R-squared, instead of returning numeric(0).
• grf.bw() gains verbose = TRUE, allowing bandwidth-search progress messages to be suppressed in tests, vignettes and batch scripts.
• grf.bw() now caps an over-large mtry at the number of predictors and uses a one-predictor-safe default.
• rf.mtry.optim() gains the arguments num.trees, cv.repeats, num.threads and verbose, and supports three evaluation strategies via cv.method: "oob" (default), "cv", "repeatedcv".
• rf.mtry.optim() no longer returns a caret train object. The new return value is a list with best.mtry, results, cv.method, num.trees and call. Code that read out$bestTune$mtry should now read out$best.mtry.

Documentation, Examples and Tests

• Added a tests/testthat/ test suite (testthat 3rd edition) covering the public API of every exported function.
• Added a vignette, vignettes/SpatialML.Rmd, with a complete workflow: mtry tuning, bandwidth search, GRF fit, local-importance plotting and prediction at new locations.
• The vignette now uses the knitr::knitr engine with markdown output, avoiding the RStudio/Quarto/Pandoc probe path during local package checks.
• The \donttest{} examples in grf.Rd, grf.bw.Rd, predict.grf.Rd and rf.mtry.optim.Rd now use small, single-threaded examples suitable for CRAN checks. Heavier Income demonstrations are kept in \dontrun{} blocks.
• The maintainer’s website (https://stamatisgeoai.eu/) is linked from the package help page and vignette in addition to DESCRIPTION.

SpatialML 1.7.0

CRAN compliance

• Fixed the missing package anchors in Rd cross-references that triggered the CRAN archival on 2025-11-28: every \link{ranger} is now \link[ranger]{ranger} (in grf.Rd, grf.bw.Rd, predict.grf.Rd and SpatialML-package.Rd).
• Moved the heavy run-time dependencies (ranger, caret, randomForest) from Depends to Imports in line with current CRAN best practice. Depends now contains only the minimum R version.
• Replaced exportPattern("^[[:alpha:]]+") in NAMESPACE with explicit export() statements; removed the unused import(randomForest) in favour of a narrow importFrom().
• Added explicit stats:: and graphics:: namespacing to all internal calls (predict, formula, terms, setNames, dist, sd, rnorm, rpois, runif, plot).
• Added a package-level help page (?SpatialML).
• Switched URLs and DOIs in the documentation to the canonical \doi{...} macro.

Bug fixes

• grf(): pre-allocation of the local-forest list previously read as.list(rep(NA, length(ntrees))), producing a one-element list because ntrees is a scalar; fixed to vector("list", Obs).
• grf(): replaced subset(DataSetSorted, DNeighbour <= bw) with bracket indexing to avoid the standard CRAN check NOTE about non-standard evaluation.
• grf(): the function now returns an object of class "grf" so that predict(obj, ...) dispatches correctly to predict.grf() without the user having to call the S3 method directly.
• random.test.data(): when dep.var.dis = "normal" the dependent variable is now drawn with rnorm() (was incorrectly drawn with runif()).
• random.test.data(): fixed the operator-precedence bug for (i in 1:vars.no - 1) (which evaluated to 0:(vars.no - 1)); the predictor matrix is now filled vectorised and column-named X1, X2, ....
• predict.grf(): replaced object[[1]] by object$Global.Model; validates that object$Forests exists and that x.var.name / y.var.name match columns of new.data; ... is now propagated to both global and local predict() calls.
• rf.mtry.optim(): caret::trainControl() arguments are now passed by name (cv.method was previously matched positionally and could land in the wrong slot).

New features and improvements

• grf(), grf.bw(), random.test.data(): added match.arg()-based validation for the kernel and dep.var.dis arguments and explicit validation of coords, bw, ntree, dataset and step.
• grf(): improved diagnostic stop messages when the fixed bandwidth yields too few neighbours.
• grf(): returned LocalModelSummary now also reports AIC and AICc for both OOB and predicted residuals.
• rf.mtry.optim(): new plot.it = TRUE argument lets the user disable plotting when running non-interactively or in vignettes.
• Removed every hard-coded set.seed() from inside the package functions (CRAN policy). Reproducibility is now under user control via a set.seed() call before each function.

Documentation

• Fixed typos: “Radmom” -> “Random”, “lcoal” -> “local”, “Neightbours” -> “Neighbours”, “tradition” -> “traditional”.
• grf.Rd: the \value{} block was renamed LocalModelSummary to match the actual returned name (was incorrectly lModelSummary).
• grf.bw.Rd: the worked example used weighted = TRUE (a non-existent argument); replaced with geo.weighted = TRUE.
• predict.grf.Rd: examples now call predict() (which dispatches via the new "grf" class) instead of the explicit predict.grf().
• Expanded the Description field of every Rd file with the bi-square weighting equation and a step-by-step description of the algorithm.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.