S3 and S4 functions are implemented for spatial multi-site
    stochastic generation of daily time series of temperature and
    precipitation. These tools make use of Vector AutoRegressive models (VARs).
    The weather generator model is then saved as an object and is calibrated by
    daily instrumental "Gaussianized" time series through the 'vars' package
    tools. Once obtained this model, it can it can be used for weather
    generations and be adapted to work with several climatic monthly time
    series.

A Bayesian credible interval is interpreted with respect to posterior probability, 
    and this interpretation is far more intuitive than that of a frequentist confidence interval. 
    However, standard highest-density intervals can be wide due to between-subjects variability and tends 
    to hide within-subject effects, rendering its relationship with the Bayes factor less clear 
    in within-subject (repeated-measures) designs. 
    This urgent issue can be addressed by using within-subject intervals in within-subject designs, 
    which integrate four methods including the Wei-Nathoo-Masson (2023) <doi:10.3758/s13423-023-02295-1>, 
    the Loftus-Masson (1994) <doi:10.3758/BF03210951>, 
    the Nathoo-Kilshaw-Masson (2018) <doi:10.1016/j.jmp.2018.07.005>, 
    and the Heck (2019) <doi:10.31234/osf.io/whp8t> interval estimates.

A robust clustering algorithm (Model-Based) similar to Expectation
    Maximization for finite mixture normal distributions is implemented, 
    its main advantage is that the estimator is resistant to outliers, 
    that means that results of parameter estimation are still correct when there
    are atypical values in the sample 
    (see Gonzalez, Maronna, Yohai and Zamar (2021) 
    <arxiv:2102.06851>).

Multiscale Curvature Classification of ground returns in 3-D LiDAR 
    point clouds, designed for forested environments. 'RMCC' is a porting to R of the 
    'MCC-lidar' method by Evans and Hudak (2007) <doi:10.1109/TGRS.2006.890412>.