Processing of large-in-memory/large-on disk rasters and spatial
	vectors using 'GRASS' <https://grass.osgeo.org/>. Most functions in the
	'terra' package are	recreated. Processing of medium-sized and smaller
	spatial objects will nearly always be faster using 'terra' or 'sf', but
	for large-in-memory/large-on-disk objects, 'fasterRaster' may be faster.
	To use most of the functions, you must have the stand-alone version (not
	the 'OSGeoW4' installer version) of 'GRASS' 8.0 or higher.

Implementation of the fast univariate inference approach (Cui et al. (2022) <doi:10.1080/10618600.2021.1950006>, Loewinger et al. (2024) <doi:10.7554/eLife.95802.2>) for fitting functional mixed models. User guides and Python package information can be found at <https://github.com/gloewing/photometry_FLMM>.

Provides fast moving-window ("focal") and buffer-based extraction 
    for raster data using the 'terra' package. Automatically selects between 
    a 'C++' backend (via 'terra') and a Fast Fourier Transform (FFT) backend 
    depending on problem size. The FFT backend supports sum and mean, while 
    other statistics (e.g., median, min, max, standard deviation) are handled 
    by the 'terra' backend. Supports multiple kernel types (e.g., circle, 
    rectangle, gaussian), with NA handling consistent with 'terra' via 
    'na.rm' and 'na.policy'. Operates on 'SpatRaster' objects and returns 
    results with the same geometry.

Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes.  See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), <doi:10.48550/arXiv.2407.10089>; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), <doi:10.48550/arXiv.2501.01324>.