Provides a drop-in replacement for rasterize() from the 'raster'
   package that takes polygon vector or data frame objects, and is much faster. 
   There is support for the main options provided by the rasterize() function, 
   including setting the field used and background value, and options for 
   aggregating multi-layer rasters. Uses the scan line algorithm attributed to
   Wylie et al. (1967) <doi:10.1145/1465611.1465619>.

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.

Fits functional generalized estimating equations for longitudinal
    functional outcomes and covariates using a one-step estimator that is fast
    even for large cluster sizes or large numbers of clusters. The package
    supports quasi-likelihoods derived from a range of families, common link
    functions, and several working correlation structures. Uncertainty
    quantification is based on sandwich variance estimators and bootstrap
    procedures that remain valid even when the working correlation is
    incorrectly specified. The package provides an implementation of the 
    method described in Loewinger et al. (2025) <https://pmc.ncbi.nlm.nih.gov/articles/PMC12306803/>. For irregularly spaced AR(1) precision matrices, the package can optionally use 
    the archived package 'irregulAR1'; if needed, it can be obtained from the CRAN
    Archive at <https://cran.r-project.org/src/contrib/Archive/irregulAR1/>.

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>, Xin et al. (2025) <doi:10.7554/eLife.109428.1>) for fitting functional mixed models. User guides and Python package information can be found at <https://github.com/gloewing/photometry_FLMM>.