R Under development (unstable) (2025-04-16 r88149 ucrt) -- "Unsuffered Consequences"
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> stopifnot(require(hdi))
Loading required package: hdi
Loading required package: scalreg
Loading required package: lars
Loaded lars 1.3

> 
> ## 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)
Nodewise regressions will be computed as no argument Z was provided.
You can store Z to avoid the majority of the computation next time around.
Z only depends on the design matrix x.
> which(fit.lasso$pval.corr < 0.05) # typically: '1' and '2' and no other
[1] 1 2
> 
> ## Group-wise testing of the first two coefficients
> fit.lasso$groupTest(1:2)
[1] 1.832905e-20
> 
> ## Hierarchical testing using distance matrix based on
> ## correlation matrix
> out.clust <- fit.lasso$clusterGroupTest()
> plot(out.clust)
> 
> ## Fit the lasso projection method without doing the preparations
> ## for group testing (saves time and memory)
> fit.lasso.faster <- lasso.proj(x, y, suppress.grouptesting = TRUE)
Nodewise regressions will be computed as no argument Z was provided.
You can store Z to avoid the majority of the computation next time around.
Z only depends on the design matrix x.
> 
> ## Use the scaled lasso for the initial estimate
> fit.lasso.scaled <- lasso.proj(x, y, betainit = "scaled lasso")
Nodewise regressions will be computed as no argument Z was provided.
You can store Z to avoid the majority of the computation next time around.
Z only depends on the design matrix x.
> which(fit.lasso.scaled$pval.corr < 0.05)
[1] 1 2
> 
> ## Use a robust estimate for the standard error
> fit.lasso.robust <- lasso.proj(x, y, robust = TRUE)
Nodewise regressions will be computed as no argument Z was provided.
You can store Z to avoid the majority of the computation next time around.
Z only depends on the design matrix x.
> which(fit.lasso.robust$pval.corr < 0.05)
[1] 1 2
> 
> ## Perform the Z&Z version of the lasso projection method
> fit.lasso <- lasso.proj(x, y, do.ZnZ = TRUE)
Nodewise regressions will be computed as no argument Z was provided.
You can store Z to avoid the majority of the computation next time around.
Z only depends on the design matrix x.
> 
> which(fit.lasso$pval.corr < 0.05) # typically: '1' and '2' and no other
[1] 1 2
> 
> proc.time()
   user  system elapsed 
   5.45    0.40    5.82