Provides a framework for quality-aware analysis of ground-based
    phenological data from the PEP725 Pan-European Phenology Database
    (Templ et al. (2018) <doi:10.1007/s00484-018-1512-8>;
    Templ et al. (2026) <doi:10.1111/nph.70869>) and similar observation
    networks. Implements station-level data quality grading, outlier
    detection, phenological normals (climate baselines), anomaly detection,
    elevation and latitude gradient estimation with robust regression,
    spatial synchrony quantification, partial least squares (PLS) regression
    for identifying temperature-sensitive periods, and sequential
    Mann-Kendall trend analysis. Supports data import from PEP725 files,
    conversion of user-supplied data, and downloadable synthetic datasets
    for teaching without barriers of registration.
    All analysis outputs provide 'print', 'summary', and
    'plot' methods. Interactive spatial visualization is available via
    'leaflet'.

Allows to perform the tests of equal predictive accuracy for panels of forecasts. Main references: Qu et al. (2024) <doi:10.1016/j.ijforecast.2023.08.001> and Akgun et al. (2024) <doi:10.1016/j.ijforecast.2023.02.001>.

Performs Bayesian variable selection under normal linear
          models for the data with the model parameters following as prior distributions either 
          the power-expected-posterior (PEP) or the intrinsic (a special case of the former)
          (Fouskakis and Ntzoufras (2022) <doi: 10.1214/21-BA1288>,
          Fouskakis and Ntzoufras (2020) <doi: 10.3390/econometrics8020017>).          
          The prior distribution on model space is the uniform over all models
          or the uniform on model dimension (a special case of the beta-binomial prior). 
          The selection is performed by either implementing a full enumeration 
          and evaluation of all possible models or using the Markov Chain 
          Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) <doi: 10.2307/1403615>). 
          Complementary functions for hypothesis testing, estimation and 
          predictions under Bayesian model averaging, as well as, plotting and 
          printing the results are also provided. The results can be compared to the
          ones obtained under other well-known priors on model parameters and model spaces.