Geospatial data computation is parallelized by grid, hierarchy,
    or raster files. Based on 'future' (Bengtsson, 2024 <doi:10.32614/CRAN.package.future>)
    and 'mirai' (Gao et al., 2025 <doi:10.32614/CRAN.package.mirai>) parallel back-ends,
    'terra' (Hijmans et al., 2025 <doi:10.32614/CRAN.package.terra>) and
    'sf' (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>)
    functions as well as convenience functions in the package can be distributed
    over multiple threads. The simplest way of parallelizing generic geospatial
    computation is to start from par_pad_*() functions to par_grid(),
    par_hierarchy(), or par_multirasters() functions. Virtually any functions
    accepting classes in 'terra' or 'sf' packages can be used in the three
    parallelization functions. A common raster-vector overlay operation
    is provided as a function extract_at(), which uses
    'exactextractr' (Baston, 2023 <doi:10.32614/CRAN.package.exactextractr>), with
    options for kernel weights for summarizing raster values at vector geometries.
    Other convenience functions for vector-vector operations including
    simple areal interpolation (summarize_aw()) and summation of
    exponentially decaying weights (summarize_sedc()) are also provided.

Calculates permutation tests that can be powerful for comparing two groups with some positive but many zero responses (see Follmann, Fay, and Proschan <DOI:10.1111/j.1541-0420.2008.01131.x>).

Implements a changepoint-aware ensemble forecasting algorithm that combines Theta, TBATS (Trigonometric, Box-Cox transformation, ARMA errors, Trend, Seasonal components), and ARFIMA (AutoRegressive, Fractionally Integrated, Moving Average) using a product-of-experts approach for robust probabilistic prediction.

Resampling is a standard step in particle filtering and in
    sequential Monte Carlo. This package implements the chopthin resampler, which
    keeps a bound on the ratio between the largest and the smallest weights after
    resampling.