Computes the Danish Pesticide Load Indicator as described in Kudsk et al. (2018) <doi:10.1016/j.landusepol.2017.11.010> and Moehring et al. (2019) <doi:10.1016/j.scitotenv.2018.07.287> for pesticide use data. Additionally offers the possibility to directly link pesticide use data to pesticide properties given access to the Pesticide properties database (Lewis et al., 2016) <doi:10.1080/10807039.2015.1133242>.

Set of tools to automatize extraction of data on pests from 'EPPO
    Data Services' and 'EPPO Global Database' and to put them into tables with
    human readable format. Those function use 'EPPO database API', thus you 
    first need to register on <https://data.eppo.int> (free of charge).
    Additional helpers allow to download, check and connect to
    'SQLite EPPO database'.

A collection of R functions that are widely used by the Petersen
    Lab. Included are functions for various purposes, including evaluating the
    accuracy of judgments and predictions, performing scoring of assessments,
    generating correlation matrices, conversion of data between various types,
    data management, psychometric evaluation, extensions related to latent
    variable modeling, various plotting capabilities, and other miscellaneous
    useful functions. By making the package available, we hope to make our
    methods reproducible and replicable by others and to help others perform
    their data processing and analysis methods more easily and efficiently. The
    codebase is provided in Petersen (2025) <doi:10.5281/zenodo.7602890> and on
    'CRAN': <doi: 10.32614/CRAN.package.petersenlab>. The package is described
    in "Principles of Psychological Assessment: With Applied Examples in R"
    (Petersen, 2024, 2025a) <doi:10.1201/9781003357421>,
    <doi:10.25820/work.007199>, <doi:10.5281/zenodo.6466589> and in "Fantasy
    Football Analytics: Statistics, Prediction, and Empiricism Using R"
    (Petersen, 2025b).

Two-sample power-enhanced mean tests, covariance tests, and simultaneous tests on mean vectors and covariance matrices for high-dimensional data. Methods of these PE tests are presented in 
    Yu, Li, and Xue (2022) <doi:10.1080/01621459.2022.2126781>; 
    Yu, Li, Xue, and Li (2022) <doi:10.1080/01621459.2022.2061354>.