Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.

Implements a semi-supervised learning framework for finite mixture
    models under a mixed-missingness mechanism. The approach models both
    missing completely at random (MCAR) and entropy-based missing at random
    (MAR) processes using a logistic–entropy formulation. Estimation is carried
    out via an Expectation–-Conditional Maximisation (ECM) algorithm with robust
    initialisation routines for stable convergence. The methodology relates to
    the statistical perspective and informative missingness behaviour discussed
    in Ahfock and McLachlan (2020) <doi:10.1007/s11222-020-09971-5> and
    Ahfock and McLachlan (2023) <doi:10.1016/j.ecosta.2022.03.007>. The package
    provides functions for data simulation, model estimation, prediction, and
    theoretical Bayes error evaluation for analysing partially labelled data
    under a mixed-missingness mechanism.

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.