wls: Weighted Least Squares
In KatoPachi/discreteRD: What the Package Does (One Line, Title Case)

Weighted Least Squares

1	wls(y, x, w, se = "HC0", cluster, cholesky = TRUE)

`y`	outcome vector.
`x`	design matrix
`w`	weight matrix
`se`	character. How to calculate robust variance-covariance matrix ("HC0", "HC1", "HC2", "HCj", "HC3", and "HC4") If "standard", calculate conventional variance-covariance matrix
`cluster`	cluster variable.
`cholesky`	logical (default is TRUE). When solving normal equation, use cholesky decomposition.

## Not run: 
x <- cbind(x1 = rnorm(100), x2 = rnorm(100))
y <- x[, 1] + 2 * x[, 2] + rnorm(100)
w <- sample(c(1, 0.5), 100, TRUE)
est <- lm_internal(y, x)
est$estimate

## End(Not run)