Implements cost-benefit analysis primitives from HM
    Treasury Green Book guidance (HM Treasury, 2022, 2026):
    the kinked Social Time Preference Rate ('STPR'), discount factors,
    net present value ('NPV'), equivalent annual cost, and real-terms
    rebasing using the GDP deflator. Designed for UK central government
    appraisal and evaluation. Bundled parameter tables carry vintage
    metadata for reproducibility.

Implements a method of iteratively collapsing the rows of a
    contingency table, two at a time, by selecting the pair of categories whose
    combination yields a new table with the smallest loss of chi-squared, as
    described by Greenacre, M.J. (1988) <doi:10.1007/BF01901670>. The result is
    compatible with the class of object returned by the 'stats' package's
    hclust() function and can be used similarly (plotted as a dendrogram,
    cut, etc.). Additional functions are provided for automatic cutting and
    diagnostic plotting.

These Bayesian models written in the 'Stan' probabilistic language can be used to interpret green crab trapping and environmental DNA monitoring data, either independently or jointly. Detailed model information is found in Keller (2022) <doi:10.1002/eap.2561>.

Tools and methods to apply the model Geospatial Regression Equation
    for European Nutrient losses (GREEN); 
    Grizzetti et al. (2005) <doi:10.1016/j.jhydrol.2004.07.036>; 
    Grizzetti et al. (2008);
    Grizzetti et al. (2012) <doi:10.1111/j.1365-2486.2011.02576.x>; 
    Grizzetti et al. (2021) <doi:10.1016/j.gloenvcha.2021.102281>.

Provides tools for downloading, processing, and reporting daily and finalized 'GreenFeed' data.

Quantification, analysis, and visualization of urban greenness within city networks using data from 'OpenStreetMap' <https://www.openstreetmap.org>.

Provides a set of accessible and automated functions to apply 
    statistical models such as Simple Linear Regression (RLS, from the Spanish 
    'Regresión Lineal Simple'), Multiple Linear Regression (RLM, from the Spanish 
    'Regresión Lineal Múltiple'), Generalized Linear Models (GLM), and time 
    series analysis through Autoregressive Integrated Moving Average (ARIMA) 
    models. Designed to support teaching at the Universidad Autónoma Chapingo, 
    it facilitates results interpretation and assumption validation through 
    automatic graphical diagnostics. Methods for regression and time series 
    are based on Montgomery et al. (2021, ISBN:978-1119570141) and 
    Box & Jenkins (1970, ISBN:978-0816211043).