NEWS

SpaNorm 1.8.0

SpaNorm() gains a BPPARAM argument to parallelise normalisation across workers via BiocParallel, accelerating large datasets. It defaults to BiocParallel::SerialParam() (no parallelisation), and results are identical regardless of the backend used.
SpaNorm() now normalises DelayedArray-backed count assays (e.g. disk-backed via HDF5Array) block-wise, so out-of-core datasets are processed without ever loading the full matrix into memory. Results match the in-memory path.
Exported fitNB(), which fits a per-gene negative binomial GLM over an arbitrary design matrix using SpaNorm's IRLS engine (with optional ridge regularisation and adjustable outlier winsorisation). This exposes the model-fitting machinery for reuse independently of SpaNorm's spatial model.

The optional GPU backend now uses the torch package instead of TensorFlow, adding native support for NVIDIA CUDA and Apple Silicon (Metal/MPS) devices and removing the Python/reticulate dependency. Users of backend = "gpu" should install torch in place of tensorflow.
The dispersion winsorisation used during normalisation now clamps at 4 MAD (previously 3), matching the coefficient and mean winsorisation, and is configurable via the winsorisation controls on the fitting/normalisation helpers.

Added model-based spatially variable gene (SVG) calling.
Added spatial visualisation funciton plotSpatial to visualise colData, gene expression, and reduced dimensions.
Added spatial visualisation function plotCovariate to visualise the biolgy, batch, and library size functions estimated by SpaNorm.
Dynamic calculation of df.tps for rectangular tissue sections.
Allow separate specification of df.tps for biology and library size.
Added GLM-PCA approximation through the SpaNormPCA function. The null model is considered to consist of the library size effects, batch effects, and the gene mean.