global_lm: Global Linear Least Squares to Estimate Local ATE
In KatoPachi/discreteRD: What the Package Does (One Line, Title Case)

Estimate the local ATE at the treatment assignment threshold by the global least squares method.

global_lm(
  basemod,
  covmod,
  data,
  subset,
  weights,
  cluster,
  submod,
  onlydmod = TRUE,
  order = c(1, 2),
  cutoff,
  assign,
  se = "HC0",
  cholesky = TRUE
)

`basemod`	baseline formula. `outcome ~ running variable`.
`covmod`	(list of) one-sided formula with covariates on rhs. If NULL, covariates are not controlled. If missing, find `options("discRD.xmod")`
`data`	data.frame which you want to use.
`subset`	subset condition.
`weights`	weight variable.
`cluster`	cluster variable.
`submod`	numeric vector. Which baseline model you want to use.
`onlydmod`	logical (default is TRUE). Whether to estimate a model without covariates.
`order`	numeric vector of global polynomial orders.
`cutoff`	numeric of cutoff points If missing, try to find `getOption("discRD.cutoff")`.
`assign`	assignment rule of treatment. If "greater", treated whose running variable is greater than or equal to cutoff. If "smaller", treated whose running variable is less than or equal to cutoff. If missing, try to find `getOption("discRD.assign")`.
`se`	character. How to calculate robust variance-covariance matrix ("HC0", "HC1", "HC2", "HCj", "HC3", and "HC4") If "standard", calculate conventional variance-covariance matrix
`cholesky`	logical (default is TRUE). When solving normal equation, use cholesky decomposition.

running <- sample(1:100, size = 1000, replace = TRUE)
cov1 <- rnorm(1000, sd = 2); cov2 <- rnorm(1000, mean = -1)
y0 <- running + cov1 + cov2 + rnorm(1000, sd = 10)
y1 <- 2 + 1.5 * running + cov1 + cov2 + rnorm(1000, sd = 10)
y <- ifelse(running <= 50, y1, y0)
bin <- ifelse(y > mean(y), 1, 0)
w <- sample(c(1, 0.5), size = 1000, replace = TRUE)
raw <- data.frame(y, bin, running, cov1, cov2, w)

set_optDiscRD(
  y + bin ~ running,
  covmod = list(~cov1, ~ cov1 + cov2),
  discRD.cutoff = 50,
  discRD.assign = "smaller"
)

global <- global_lm(data = raw)
str(global_lm(submod = 1, data = raw))
str(global_lm(submod = 1, order = 3, data = raw))
str(global_lm(data = raw, cutoff = 30))
est2 <- global_lm(data = raw, se = "HC1", weights = w)