## Not run:
# #See ?'crisp-package' for a full example of how to use this package
# #generate data (using a very small 'n' for illustration purposes)
# set.seed(1)
# data <- sim.data(n = 15, scenario = 2)
#
# #fit model for a range of tuning parameters, i.e., lambda values
# #lambda sequence is chosen automatically if not specified
# crisp.out <- crisp(X = data$X, y = data$y)
# #or fit model and select lambda using 2-fold cross-validation
# #note: use larger 'n.fold' (e.g., 10) in practice
# crispCV.out <- crispCV(X = data$X, y = data$y, n.fold = 2)
#
# #summarize all of the fits
# summary(crisp.out)
# #or just summarize a single fit
# #we examine the fit with an index of 25. that is, lambda of
# crisp.out$lambda.seq[25]
# summary(crisp.out, lambda.index = 25)
# #lastly, we can summarize the fit chosen using cross-validation
# summary(crispCV.out)
# ## End(Not run)
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