Algorithms for the spatial stratification of landscapes, sampling and modeling of 
    spatially-varying phenomena. These algorithms offer a simple framework for the stratification 
    of geographic space based on raster layers representing landscape factors and/or factor scales. 
    The stratification process follows a hierarchical approach, which is based on first level units 
    (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric 
    techniques allow to measure the correspondence between the geographic space and the landscape 
    configuration represented by the units. These correspondence metrics are useful to define 
    sampling schemes and to model the spatial variability of environmental phenomena. The 
    theoretical background of the algorithms and code examples are presented in Fuentes, Dorantes,
    and Tipton (2021). <doi:10.31223/X50S57>.

Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package <https://CRAN.R-project.org/package=terra>.

R-based access to a large set of data variables relevant to forest ecology in British Columbia (BC), Canada. Layers
    are in raster format at 100m resolution in the BC Albers projection, hosted at the Federated Research Data Repository (FRDR)
    with <doi:10.20383/101.0283>. The collection includes: elevation; biogeoclimatic zone; wildfire; cutblocks; forest attributes from
    Hansen et al. (2013) <doi:10.1139/cjfr-2013-0401> and Beaudoin et al. (2017) <doi:10.1139/cjfr-2017-0184>; and rasterized
    Forest Insect and Disease Survey (FIDS) maps for a number of insect pest species, all covering the period 2001-2018.
    Users supply a polygon or point location in the province of BC, and 'rasterbc' will download the overlapping raster tiles
    hosted at FRDR, merging them as needed and returning the result in R as a 'SpatRaster' object. Metadata associated with these
    layers, and code for downloading them from their original sources can be found in the 'github' repository
    <https://github.com/deankoch/rasterbc_src>.