Provides flexible hazard ratio curves allowing non-linear
  relationships between continuous predictors and survival.
  To better understand the effects that each continuous covariate
  has on the outcome, results are expressed in terms of hazard
  ratio curves, taking a specific covariate value as reference.
  Confidence bands for these curves are also derived.

Implementation of the SIC epsilon-telescope method, either
    using single or distributional (multiparameter) regression. Includes classical regression
    with normally distributed errors and robust regression, where the errors are from
    the Laplace distribution. The "smooth generalized normal distribution" is used,
    where the estimation of an additional shape parameter allows the user to move
    smoothly between both types of regression. See O'Neill and Burke (2022)
    "Robust Distributional Regression with Automatic Variable Selection" for more details.
    <doi:10.48550/arXiv.2212.07317>. This package also contains the data analyses from O'Neill and
    Burke (2023). "Variable selection using a smooth information criterion for distributional
    regression models". <doi:10.1007/s11222-023-10204-8>.

Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).

Some M-estimators for 1-dimensional location (Bisquare,
             ML for the Cauchy distribution, and the estimators from 
             application of the smoothing principle introduced in Hampel, 
             Hennig and Ronchetti (2011) to the above, the Huber
             M-estimator, and the median, main function is smoothm), 
             and Pitman estimator.

Performs the Partial Least-Squares ('PLS') algorithm for
    functional data through the concept of active area integration. This 
    approach builds upon the basis expansion methods for functional 'PLS' 
    regression described in Aguilera et al. (2010) 
    <doi:10.1016/j.chemolab.2010.09.007>. The package seamlessly handles both 
    Scalar Functional Data ('SFD') and Categorical Functional Data ('CFD'), 
    providing interpretable regression curves even for discrete 
    state changes. It was developed during a PhD thesis between 'DECATHLON' 
    and French research institute 'INRIA' 2022-2026. The 'SmoothPLS' method does not directly
    decompose the data into a basis; rather, it assumes the data is known as 
    precisely as desired, and for every 'PLS' component, the weight functions are 
    decomposed into the basis. For both single-state and multi-state 'CFD' 
    as well as 'SFD', the algorithm is implemented for a scalar response. 
    To provide a baseline, a naive 'PLS' method on time-value functions and 
    standard Functional 'PLS' are also implemented.