## Not run:
# # load data set
# data(GLESsmall)
#
# # define response and covariate matrix
# X <- scale(GLESsmall[, 11:14])
# Y <- as.matrix(GLESsmall[, 1:10])
#
# # vector of subtitles, containing the coding of the single covariates
# subs <- c("(in years)","female (1); male (0)",
# "East Germany (1); West Germany (0)","(very) good (1); else (0)")
#
# # vector of tuning parameters
# lambda <- exp(seq(log(31),log(1),length=50))-1
#
# # compute BTLLasso model
# m <- BTLLasso(Y = Y, X = X, lambda = lambda)
#
# op <- par(no.readonly = TRUE)
# par(mar=c(5,4,4,8))
#
# # plot covariate paths
# paths(m)
#
# # plot parameter paths
# singlepaths(m, subs = subs)
#
# # compute 10-fold cross-validation
# set.seed(5)
# m.cv <- cv.BTLLasso(Y = Y, X = X, folds = 10, lambda = lambda, cores = 10)
#
# # plot covariate paths, together with cv-optimal model
# paths(m.cv)
#
# # plot parameter paths, together with cv-optimal model
# singlepaths(m.cv, subs = subs)
#
# # compute bootstrap confidence intervals
# m.boot <- boot.BTLLasso(m.cv, B = 100, cores = 25)
#
# # plot bootstrap confidence intervals
# par(mar=c(5,5,4,3))
# ci.BTLLasso(m.boot, subs = subs)
#
# par(op)
# ## End(Not run)
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