Creates an S4 class "SSM" and defines functions for fitting smooth
    supersaturated models, a polynomial model with spline-like behaviour.
    Functions are defined for the computation of Sobol indices for sensitivity
    analysis and plotting the main effects using FANOVA methods. It also
    implements the estimation of the SSM metamodel error using a GP model with
    a variety of defined correlation functions.

Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.

In order to facilitate the adjustment of the sample selection models
  existing in the literature, we created the 'ssmodels' package. Our package
  allows the adjustment of the classic Heckman model (Heckman (1976),
  Heckman (1979) <doi:10.2307/1912352>), and the estimation of the parameters of
  this model via the maximum likelihood method and two-step method, in addition
  to the adjustment of the Heckman-t models introduced in the literature by
  Marchenko and Genton (2012) <doi:10.1080/01621459.2012.656011> and the
  Heckman-Skew model introduced in the literature by Ogundimu and Hutton (2016)
  <doi:10.1111/sjos.12171>. We also implemented functions to adjust the
  generalized version of the Heckman model, introduced by Bastos, Barreto-Souza,
  and Genton (2021) <doi:10.5705/ss.202021.0068>, that allows the inclusion of
  covariables to the dispersion and correlation parameters, and a function to
  adjust the Heckman-BS model introduced by Bastos and Barreto-Souza (2020)
  <doi:10.1080/02664763.2020.1780570> that uses the Birnbaum-Saunders
  distribution as a joint distribution of the selection and primary regression
  variables. This package extends and complements existing R packages such as 
  'sampleSelection' (Toomet and Henningsen, 2008) and 'ssmrob' (Zhelonkin et al., 2016), providing additional robust and flexible sample selection models.

Estimates previously compiled state-space modeling for mouse-tracking experiments using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation.