spike_and_slab_normal: Group spike and slab variable selection with Gaussian outcome
In IQSS/moretrees: Multi-Outcome Regression with Tree-Structured Shrinkage

Description Usage Arguments Details Value Model Description See Also

Here's a brief description. spike_and_slab_normal performs group variable selection via a spike and slab prior for continuous, normally distributed data. The posterior is approximated via variational inference. This function returns the parameters of the variational approximation.

spike_and_slab_normal(
  dsgn,
  initial_values,
  tol,
  max_iter,
  update_hyper,
  update_hyper_freq,
  hyper_fixed,
  print_freq,
  hyper_random_init,
  vi_random_init
)

`tol`	Convergence tolerance for ELBO.
`max_iter`	Maximum number of iterations of the VI algorithm.
`update_hyper`	Update hyperparameters? Default = TRUE.
`update_hyper_freq`	How frequently to update hyperparameters. Default = every 50 iterations.
`print_freq`	How often to print out iteration number.
`y`	Numeric vector of length n of outcome data
`X`	Matrix of dimension n x sum(K), where n is the number of units, and K[g] is the number of variables in group g.
`groups`	A list of length G (number of groups), where groups[[g]] is an integer vector specifying the columns of X that belong to group g.
`W`	Matrix of data with non-sparse regression coefficients of dimension n x m
`nrestarts`	Number of random re-starts of the VI algorithm. The result that gives the highest ELBO will be returned. It is recommended to choose nrestarts > 1.