Data structures and methods to work with web tracking data. The functions cover data preprocessing steps, enriching web tracking data with external information and methods for the analysis of digital behavior as used in several academic papers (e.g., Clemm von Hohenberg et al., 2023 <doi:10.17605/OSF.IO/M3U9P>; Stier et al., 2022 <doi:10.1017/S0003055421001222>).

A Stata-style `webuse()` function for importing named datasets from Stata's online collection.

Parses http request data in application/json, multipart/form-data, 
    or application/x-www-form-urlencoded format. Includes example of hosting
    and parsing html form data in R using either 'httpuv' or 'Rhttpd'.

Provides functions to create factor variables with contrasts based on weighted effect coding, and their interactions. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different.

Makes research involving EMDAT and related datasets easier. These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions.

Provides functions for analysing Water-Energy-Food-Nutrient-Carbon
    (WEFNC) nexus interactions in agricultural production systems. Includes
    functions for computing water use efficiency (WUE), water productivity (WP),
    and water footprint (WF) including green, blue, and grey components following
    the methodology of Hoekstra et al. (2011, ISBN:9781849712798). Includes energy
    budgeting tools for energy use efficiency (EUE), energy return on investment
    (EROI), net energy (NE), and energy productivity (EP). Computes nutrient use
    efficiency (NUE) metrics including agronomic efficiency (AE), physiological
    efficiency (PE), recovery efficiency (RE), and partial factor productivity
    (PFP) as defined by Dobermann (2007)
    <https://digitalcommons.unl.edu/agronomyfacpub/316/> and
    Congreves et al. (2021) <doi:10.3389/fpls.2021.637108>. Estimates carbon
    footprint (CF), greenhouse gas (GHG) emissions, soil organic carbon (SOC)
    stocks, and global warming potential (GWP) using Intergovernmental Panel on
    Climate Change (IPCC) Sixth Assessment Report (AR6) default values
    (CH4 = 27, N2O = 273) as reported in Forster et al. (2021)
    <doi:10.1017/9781009157896.009>. Computes composite
    Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus indices,
    trade-off correlation matrices, and generates radar and heatmap
    visualizations for comparing agricultural treatments. Supports conservation
    agriculture (CA), irrigated and rain-fed systems, and arid and semi-arid
    production environments. Methods follow Lal (2004)
    <doi:10.1016/j.envint.2004.03.005> for carbon emissions from farm
    operations, and Hoover et al. (2023) <doi:10.1016/j.scitotenv.2022.160992>
    for water use efficiency indicators.