Provides an R scripting interface to the open-source 'SAGA-GIS' 
    (System for Automated Geoscientific Analyses Geographical Information
    System) software. 'Rsagacmd' dynamically generates R functions for every
    'SAGA-GIS' geoprocessing tool based on the user's currently installed
    'SAGA-GIS' version. These functions are contained within an S3 object
    and are accessed as a named list of libraries and tools. This structure
    facilitates an easier scripting experience by organizing the large number
    of 'SAGA-GIS' geoprocessing tools (>700) by their respective library.
    Interactive scripting can fully take advantage of code autocompletion tools
    (e.g. in 'RStudio'), allowing for each tools syntax to be quickly
    recognized. Furthermore, the most common types of spatial data (via the
    'terra', 'sp', and 'sf' packages) along with non-spatial data are
    automatically passed from R to the 'SAGA-GIS' command line tool for
    geoprocessing operations, and the results are loaded as the appropriate R
    object. Outputs from individual 'SAGA-GIS' tools can also be chained using
    pipes from the 'magrittr' and 'dplyr' packages to combine complex
    geoprocessing operations together in a single statement. 'SAGA-GIS' is
    available under a GPLv2 / LGPLv2 licence from
    <https://sourceforge.net/projects/saga-gis/> including Windows x86/x64
    and macOS binaries. SAGA-GIS is also included in Debian/Ubuntu default software
    repositories. Rsagacmd has currently been tested on 'SAGA-GIS' versions
    from 2.3.1 to 9.5.1 on Windows, Linux and macOS.

Classes and functions to create and summarize different types
    of resampling objects (e.g. bootstrap, cross-validation).

Resampling Stats (http://www.resample.com) is an add-in for
    running randomization tests in Excel worksheets. The workflow is (1) to define
    a statistic of interest that can be calculated from a data table, (2) to
    randomize rows ad/or columns of a data table to simulate a null hypothesis
    and (3) and to score the value of the statistic from many randomizations. The
    relative frequency distribution of the statistic in the simulations is then
    used to infer the probability of the observed value be generated by the null
    process (probability of Type I error). This package intends to translate this
    logic for R for teaching purposes. Keeping the original workflow is favored over
    performance.