An overall test for seasonality of a given time
    series in addition to a set of individual seasonality tests as
    described by Ollech and Webel (forthcoming): An overall seasonality
    test. Bundesbank Discussion Paper.

Provides tools for processing and evaluating seasonal weather forecasts, 
    with an emphasis on tercile forecasts. We follow the World Meteorological Organization's 
    "Guidance on Verification of Operational Seasonal Climate Forecasts", 
    S.J.Mason (2018, ISBN: 978-92-63-11220-0, URL: <https://library.wmo.int/idurl/4/56227>). 
    The development was supported by the European Union’s Horizon 2020 research and innovation 
    programme under grant agreement no. 869730 (CONFER).
    A comprehensive online tutorial is available at <https://seasonalforecastingengine.github.io/SeaValDoc/>.

Computes and integrates surface energy balance components of
    evapotranspiration (ET), sensible heat (H), soil heat flux (G) and net
    radiation (Rn) into the Food and Agriculture Organization (FAO)
    Irrigation and Drainage Paper 56 (FAO-56) water balance model. The
    package can perform single crop coefficient (Kc), dual Kc and the
    integration of thermal-based evaporative fractions in a water balance
    model. The surface energy balance models include Two-Source Surface
    Energy Balance (TSEB) models, namely the Priestley-Taylor TSEB
    (TSEB-PT), the Penman-Monteith TSEB (TSEB-PM) and TSEB-Parallel, as
    well as One-Source Surface Energy Balance (OSEB) models, namely the
    Surface Energy Balance Algorithm for Land (SEBAL), Mapping
    Evapotranspiration at high Resolution with Internalized Calibration
    (METRIC), Surface Energy Balance Index (SEBI), Simplified Surface
    Energy Balance (SSEB) and Surface Energy Balance System (SEBS). Methods
    are described in Allen et al. (2007)
    <doi:10.1061/(ASCE)0733-9437(2007)133:4(380)> and Bastiaanssen et al.
    (1998) <doi:10.1016/S0022-1694(98)00253-4>.

Monte Carlo sampling algorithms for semiparametric
    Bayesian regression analysis. These models feature a nonparametric
    (unknown) transformation of the data paired with widely-used
    regression models including linear regression, spline regression,
    quantile regression, and Gaussian processes. The transformation
    enables broader applicability of these key models, including for
    real-valued, positive, and compactly-supported data with challenging
    distributional features. The samplers prioritize computational
    scalability and, for most cases, Monte Carlo (not MCMC) sampling for
    greater efficiency. Details of the methods and algorithms are provided
    in Kowal and Wu (2024) <doi:10.1080/01621459.2024.2395586>.