Dissemination: SuperLearner -> sl3 translation guide

[nhejazi]

In order to help with user migration, it would be really nice to have a "translation guide" for experienced users of the SuperLearner package to use sl3. This would take the place of a "SuperLearner3()" style wrapper function, instead helping users to find the correct sl3 idiom(s) to use in place of a given SuperLearner function or argument. An example of such would be the following:

library(SuperLearner)
library(sl3)
set.seed(57192)

# toy data
n <- 1000
p <- 3
x <- as.data.frame(replicate(p, rnorm(n, mean = 0, sd = 1)))
y <- sin(x[, 1]) + x[, 2] + rnorm(n, mean = 0, sd = 0.3)
dat <- as.data.frame(cbind(x, y))

# using SuperLearner
sl_lib <- c("SL.mean", "SL.glm")
sl_old <- SuperLearner(Y = y, X = x, SL.library = sl_lib)
y_pred_old <- predict(sl_old)$pred

# using sl3
sl_task <- make_sl3_Task(covariates = colnames(dat)[seq_len(p)], outcome = "y", data = dat)
sl3_lib <- make_learner_stack("Lrnr_mean", "Lrnr_glm_fast")
sl_new <- Lrnr_sl$new(learners = sl3_lib, metalearner = Lrnr_nnls$new())
sl_trained <- sl_new$train(sl_task)
y_pred_new <- sl_trained$predict()

# squared error difference
mean((y_pred_old - y_pred_new)^2)

...with the point being that an experience user (of SuperLearner, new to sl3) would be able to easily pick up the similarities between, for example, make_learner_stack and SL.library or calling the R6 .$predict method of the trained sl3 model vs. the S3 predict(...) method for an object of class SuperLearner. This is just an early-stage idea and could definitely use refinement and input from further discussion.