elastic.net.lm: Fitting Linear Models with Elastic Net Penalty
In Accelerytics/mlkit: Machine Learning Kit

Implementation of the MM algorithm solver for a linear regression model with an elastic net penalty term.

elastic.net.lm(
  formula,
  data,
  lambda,
  alpha,
  intercept = F,
  standardize = F,
  beta.init = NULL,
  beta.tol = 0,
  loss.tol = 1e-06,
  eps = 1e-06,
  seed = NULL,
  verbose = 0
)

`formula`	an object of class `formula`: a symbolic description of the model to be fitted following the standard of `lm`.
`data`	an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment (`formula`), typically the environment from which this function is called.
`lambda`	penalty term scaling hyperparameter.
`alpha`	L1-weight hyperparameter in elastic net penalty term.
`intercept`	optional boolean indicating whether to fit an intercept. If `TRUE`, `standardize` is ignored. Default is `FALSE`.
`standardize`	optional boolean indicating whether to return results for standardized data. If `intercept` is `TRUE`, this argument is ignored. Default is `FALSE`. `TRUE`, `standardize` is ignored. Default is `FALSE`.
`beta.init`	optional initial beta parameters to use in the MM algorithm. Default is `NULL`.
`beta.tol`	optional absolute tolerance for rounding down parameter standardized estimates. If the absolute value of a parameter estimate in the standardized model is smaller than `beta.tol`, it is rounded down to zero. Default is `0`, that is, no rounding.
`loss.tol`	optional convergence tolerance on the elastic net loss in the MM algorithm. Default is `1e-6`.
`eps`	optional correction term to avoid rounding by zero. Default is `1e-6`.
`seed`	optional seed. Default is `NULL`.
`verbose`	optional number indicating per how many iterations the estimation progress is displayed. Default is `0`, that is, no progress updates.

elastic.net.lm returns an object of class mlkit.lm.fit. An object of class mlkit.lm.fit is a list containing at least the following components:

`coefficients`	a named vector of optimal coefficients.
`loss`	residual sum of squares plus elastic net loss for optimal coefficients.
`r2`	coefficient of determination for optimal coefficients.
`adj.r2`	adjusted coefficient of determination for optimal coefficients.