Provides tools to record, validate, and analyse soil tillage
    depth and erosion across years and field treatments. Includes functions
    for year-wise tillage operation summaries, erosion depth tracking,
    compaction detection, soil loss estimation, and visualisation of
    temporal changes in tillage and erosion profiles. Methods follow
    Lal (2001) <doi:10.1201/9780203739280> and
    Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to
    Conservation Planning with the Revised Universal Soil Loss Equation
    (RUSLE)"
    <https://ntrl.ntis.gov/NTRL/dashboard/searchResults/titleDetail/PB97153704.xhtml>.

Supervised latent-variable regression for high-dimensional predictors
    such as soil reflectance spectra. The model uses an encoder-decoder neural
    network with a stochastic Gaussian latent representation regularized by a
    Kullback-Leibler term, and a supervised prediction head trained jointly with
    the reconstruction objective. The implementation interfaces R with a 'Python'
    deep-learning backend and provides utilities for training, tuning, and
    prediction.

It implements parametric formulas of soil
    water retention or conductivity curve. At the moment, only Van Genuchten
    (for soil water retention curve) and Mualem (for hydraulic conductivity)
    were implemented. 
    See reference (<http://en.wikipedia.org/wiki/Water_retention_curve>).

Provides a simple way for utilizing Sojourn methods for
    accelerometer processing, as detailed in Lyden K, Keadle S,
    Staudenmayer J, & Freedson P (2014) <doi:10.1249/MSS.0b013e3182a42a2d>,
    Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016)
    <doi:10.1249/MSS.0000000000000915>, and Hibbing PR, Ellingson LD,
    Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.

Stores objects (e.g. neural networks) that are needed for
    using Sojourn accelerometer methods. For more information, see
    Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014)
    <doi:10.1249/MSS.0b013e3182a42a2d>, Ellingson LD, Schwabacher IJ,
    Kim Y, Welk GJ, & Cook DB (2016) <doi:10.1249/MSS.0000000000000915>,
    and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018)
    <doi:10.1249/MSS.0000000000001486>.