local_random_test_int: Mean difference test (t-test) for local random approach
In KatoPachi/discreteRD: What the Package Does (One Line, Title Case)

Mean difference test (t-test) for local random approach

1	local_random_test_int(basemod, data, bootse, bootp)

`basemod`	baseline formula. `outcome ~ running variable`.
`data`	data.frame which you want to use.
`bootse`	numeric. Generate N averages of bootstrap samples and use the standard deviation as the standard error of the average. If missing, standard error of mean is calculated by sqrt(v / e) where v is unbiased variance of outcome, and e is effective sample size: e = sum(w) ^ 2 / sum(w ^ 2). If w is missing, we set w = 1, and obtain e = 1 / n. standard error of mean difference is obtained by sqrt((v1 / e1) + (v0 / e0))
`bootp`	numeric. Perform a permutation test with N re-randomizations. The p-value is obtained at a rate where the absolute value of the mean difference due to rerandomization is greater than the absolute value of the observed mean difference. If missing, standard t-test is performed.

## Not run: 
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(discRD.cutoff = 50, discRD.assign = "smaller")
useit <- clean_rd_data(y ~ running, data = raw)
str(local_random_test_int(y ~ running, useit$data))
str(local_random_test_int(y ~ running, useit$data, bootse = 100))
str(local_random_test_int(y ~ running, useit$data, bootp = 100))

## End(Not run)