SL.hal: Highly Adaptive Lasso
In benkeser/halplus: Highly Adaptive Lasso

Description Usage Arguments

SuperLearner wrapper for hal.

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SL.hal(Y, X, newX, family = gaussian(), verbose = TRUE,
  obsWeights = rep(1, length(Y)), nfolds = ifelse(length(Y) <= 100, 20, 10),
  nlambda = 100, useMin = TRUE, ...)

`Y`	A `numeric` of outcomes
`X`	A `data.frame` of predictors
`newX`	Optional `data.frame` on which to return predicted values
`family`	Needs to have a character object in `family$family` as required by `SuperLearner`
`verbose`	A `boolean` indicating whether to print output on functions progress
`obsWeights`	Optional `vector` of observation weights to be passed to `cv.glmnet`
`nfolds`	Number of CV folds passed to `cv.glmnet`
`nlambda`	Number of lambda values to search across in `cv.glmnet`
`useMin`	Option passed to `cv.glmnet`, use minimum risk lambda or 1se lambda (more penalization)
`...`	Any other arguments to pass-through to hal()