stopifnot(require(hdi))

## this is the example code of the help file of lasso.proj

x <- matrix(rnorm(100 * 10), nrow = 100, ncol = 10)
y <- x[,1] + x[,2] + rnorm(100)
fit.lasso <- lasso.proj(x, y)
which(fit.lasso$pval.corr < 0.05) # typically: '1' and '2' and no other

## Group-wise testing of the first two coefficients
fit.lasso$groupTest(1:2)

## Hierarchical testing using distance matrix based on
## correlation matrix
out.clust <- fit.lasso$clusterGroupTest()
plot(out.clust)

## Fit the lasso projection method without doing the preparations
## for group testing (saves time and memory)
fit.lasso.faster <- lasso.proj(x, y, suppress.grouptesting = TRUE)

## Use the scaled lasso for the initial estimate
fit.lasso.scaled <- lasso.proj(x, y, betainit = "scaled lasso")
which(fit.lasso.scaled$pval.corr < 0.05)

## Use a robust estimate for the standard error
fit.lasso.robust <- lasso.proj(x, y, robust = TRUE)
which(fit.lasso.robust$pval.corr < 0.05)

## Perform the Z&Z version of the lasso projection method
fit.lasso <- lasso.proj(x, y, do.ZnZ = TRUE)

which(fit.lasso$pval.corr < 0.05) # typically: '1' and '2' and no other