Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson.  2014.  An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.

Data on the Spy vs. Spy comic strip of Mad magazine, created and
   written by Antonio Prohias.

The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. The evaluation of soil physical, chemical, and biological parameters is necessary to determine the overall soil quality index. In our package, three commonly used methods, including linear scoring, regression-based, and principal component-based soil quality indexing, are employed to calculate the soil quality index. This package has been developed using concept of Bastida et al. (2008) and Doran and Parkin (1994) <doi:10.1016/j.geoderma.2008.08.007> <doi:10.2136/sssaspecpub35.c1>.

Provides a comprehensive, modular framework for computing the
    Soil Quality Index (SQI) using six established methods: Linear Scoring
    (Doran and Parkin, 1994, <doi:10.2136/sssaspecpub35.c1>),
    Regression-based (Masto et al., 2008, <doi:10.1007/s10661-007-9697-z>),
    Principal Component Analysis-based (Andrews et al., 2004,
    <doi:10.2136/sssaj2004.1945>), Fuzzy Logic, Entropy Weighting (Shannon,
    1948, <doi:10.1002/j.1538-7305.1948.tb01338.x>), and TOPSIS (Hwang and
    Yoon, 1981, <doi:10.1007/978-3-642-48318-9>). Implements four variable
    scoring functions: more-is-better, less-is-better, optimum-value, and
    trapezoidal, following Karlen and Stott (1994,
    <doi:10.2136/sssaspecpub35.c4>). Includes automated Minimum Data Set
    selection via Principal Component Analysis with Variance Inflation Factor
    filtering (Kaiser, 1960, <doi:10.1177/001316446002000116>), one-way
    ANOVA with Tukey HSD post-hoc tests, leave-one-out sensitivity analysis,
    and publication-quality visualization using 'ggplot2'.

Runs 'SQL' statements on in-memory data frames within a temporary in-memory 'duckdb' data base.