GaussRMoE: Penalized MLE for the regularized Mixture of Experts.
In fchamroukhi/HDME: LASSO Regularized Mixture of Experts Models

This function provides a penalized MLE for the regularized Mixture of Experts (MoE) model corresponding with the penalty parameters Lambda, Gamma.

1	GaussRMoE(Xm, Ym, K, Lambda, Gamma, option = FALSE, verbose = FALSE)

`Xm`	Matrix of explanatory variables. Each feature should be standardized to have mean 0 and variance 1. One must add the column vector (1,1,...,1) for the intercept variable.
`Ym`	Vector of the response variable. For the Gaussian case Y should be standardized. For multi-logistic model Y is numbered from 1 to R (R is the number of labels of Y).
`K`	Number of experts (K > 1).
`Lambda`	Penalty value for the experts.
`Gamma`	Penalty value for the gating network.
`option`	Optional. `option = TRUE`: using proximal Newton-type method; `option = FALSE`: using proximal Newton method.
`verbose`	Optional. A logical value indicating whether or not values of the log-likelihood should be printed during EM iterations.