FWIW, the quick RCall.jl solution could be
julia> using RCall
R> library(glmnet); library(lars)
┌ Warning: RCall.jl: Loading required package: Matrix
│ Loaded glmnet 4.1-2
│ Loaded lars 1.2
│
└ @ RCall ~/.julia/packages/RCall/iMDW2/src/io.jl:160
R> data(QuickStartExample)
R> x <- QuickStartExample$x
R> y <- QuickStartExample$y
R> fit_lars <- lars(x, y, type = "lar", intercept = F, normalize = F)
R> fit_lars
Call:
lars(x = x, y = y, type = "lar", normalize = F, intercept = F)
R-squared: 0.917
Sequence of LAR moves:
Var 1 14 6 5 20 3 8 11 7 10 15 4 13 12 2 9 17 16 18 19
Step 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20