I’m looking for ways to estimate quantiles with (1) splines, (2) penalization and (3) the ability to print an equation. RMS makes it easy to get (1) and (3), but I have not found a way to implement penalization:

```
library(dplyr)
library(rms)
set.seed(123)
n <- 100
df <- data.frame(y = exp(x1 + rnorm(n)/4),
x1 = rnorm(n))
dd <- datadist(df); options(datadist='dd')
fq50 <- Rq(y ~ rcs(x1, 3), tau = 0.5, data = df)
pred.q <- Function(fq50)
sascode(pred.q)
```

I see that Roger Koenker’s original package on which `RMS::Rq()`

is based has a `method = "lasso"`

or `"scad"`

that “*…implement the lasso penalty and Fan and Li smoothly clipped absolute deviation penalty, respectively. These methods should probably be regarded as experimental*”.

In my application, I have 16 continuous predictors and n~3000.