Why can't do ridge regression with one predictor?

I’m trying to fit a ridge regression model with a single predictor. However, when I try to do so in three different R packages I get the three following errors:

Error in colMeans(X[, -Inter]) : 
  'x' must be an array of at least two dimensions

Error in if (is.null(np) | (np[2] <= 1)) stop("x should be a matrix with 2 or more columns") : 
  argument is of length zero

Error in colMeans(x[, -Inter]) : 
  'x' must be an array of at least two dimensions

The bottom line from these errors is that x needs to have at least 2 dimensions. Why is this necessary for ridge regression? Does this mean that I can’t use ridge regression with a single predictor? Just seems weird I couldn’t use ridge regression to get regularization for something like a t-test.

Here is my code:

library(lmridge)
library(glmnet)
library(ridge)

# data 
set.seet(100)
y <- rnorm(100)
x <- rbinom(100, 1, .5)
z <- rbinom(100, 1, .5)
data <- cbind.data.frame(y, x, z)

# ridge
linearRidge(y ~ x, data = data)

# glmnet
glmnet(data$x, data$y, nlambda = 25, alpha = 0, family = 'gaussian', lambda = .5)

# lmridge
lmridge(y ~ x, data = data, scaling = "sc", K = seq(0, 1, 0.001))