To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation).
    The methods used in this package are referenced from Zoran B. Popović (2017) <doi:10.21037/cdt.2017.03.12>.

Regression context for the Partial Least Squares framework for 
    Extreme values. Estimations of the Shrinkage for Extreme Partial Least-Squares 
    (SEPaLS) estimators, an adaptation of the original Partial Least Squares 
    (PLS) method tailored to the extreme-value framework.
    The SEPaLS project is a joint work by Stephane Girard, Hadrien Lorenzo and 
    Julyan Arbel.
    R code to replicate the results of the paper is available at 
    <https://github.com/hlorenzo/SEPaLS_simus>.
    Extremes within PLS was already studied by one of the authors, see M 
    Bousebeta, G Enjolras, S Girard (2023) <doi:10.1016/j.jmva.2022.105101>.

Maximum likelihood estimation of the parameters
    of matrix and 3rd-order tensor normal distributions with unstructured
    factor variance covariance matrices, two procedures, and for unbiased
    modified likelihood ratio testing of simple and double separability
    for variance-covariance structures, two procedures. References:
    Dutilleul P. (1999) <doi:10.1080/00949659908811970>, 
    Manceur AM, Dutilleul P. (2013)
    <doi:10.1016/j.cam.2012.09.017>, and Manceur AM, Dutilleul 
    P. (2013) <doi:10.1016/j.spl.2012.10.020>.

Visual representations of model fit or predictive success in the form of "separation plots."  See Greenhill, Brian, Michael D. Ward, and Audrey Sacks. "The separation plot: A new visual method for evaluating the fit of binary models." American Journal of Political Science 55.4 (2011): 991-1002.

Provides functions and methods for estimating phenological dates (green up, 
  start of a season, maturity, senescence, end of a season and dormancy) from (nearly) 
  periodic Earth Observation time series. These dates are critical points of some 
  derivatives of an idealized curve which, in turn, is obtained through a functional principal 
  component analysis-based regression model. Some of the methods implemented here are 
  based on T. Krivobokova, P. Serra and F. Rosales (2022) <https://www.sciencedirect.com/science/article/pii/S0167947322000998>. 
  Methods for handling and plotting Earth observation time series are also provided.