Fast functions implemented in C++ via 'Rcpp' to support the
    'NeuroAnatomy Toolbox' ('nat') ecosystem. These functions provide large
    speed-ups for basic manipulation of neuronal skeletons over pure R
    functions found in the 'nat' package. The expectation is that end
    users will not use this package directly, but instead the 'nat'
    package will automatically use routines from this package when it is
    available to enable large performance gains.

Provides R native 'ONNX' model inference without requiring 'Python', 'reticulate' bindings, or 'TensorFlow'. This package directly binds the 'ONNX Runtime' C API via 'Rcpp', enabling real-time inference for '.onnx' engines, all within R. Standard CPU execution is supported as well as the 'CoreML' Execution Provider (CEP) for Apple Silicon, all without external bindings. This package handles OS detection, linking 'ONNX' libraries, and inference. For more information about 'ONNX Runtime' see <https://onnxruntime.ai/>.

Provides streamlined installation for packages from the 'natverse',
    a suite of R packages for computational neuroanatomy built on top of the
    'nat' 'NeuroAnatomy Toolbox' package. Installation of the complete
    'natverse' suite requires a 'GitHub' user account and personal access token
    'GITHUB_PAT'. 'natmanager' will help the end user set this up if necessary.

Color palettes for data visualization inspired by National Parks. Currently contains 15 color schemes and checks for colorblind-friendliness of palettes.

Interface to 'NatureServe' (<https://www.natureserve.org/>).
    Includes methods to get data, image metadata, search taxonomic names,
    and make maps.

Natural strata can be used in observational studies to balance
    the distributions of many covariates across any number of treatment
    groups and any number of comparisons. These strata have proportional 
    amounts of units within each stratum across the treatments, allowing 
    for simple interpretation and aggregation across strata. Within each 
    stratum, the units are chosen using randomized rounding of a linear 
    program that balances many covariates. For more details, see Brumberg et al. (2022) <doi:10.1111/rssa.12848> and Brumberg et al.(2023) <doi:10.1093/jrsssc/qlad010>.
    To solve the linear program, the 'Gurobi' commercial optimization software 
    is recommended, but not required. The 'gurobi' R package can be installed by following the instructions at <https://docs.gurobi.com/projects/optimizer/en/current/reference/r/setup.html> after claiming your free academic license at <https://www.gurobi.com/academia/academic-program-and-licenses/>.