stopifnot(all.equal(
stat.extend::CONF.mean(.05, mtcars$mpg, N=1000),
structure(list(list(l = 7.20563283519216, r = 32.9756171648078,
lc = TRUE, rc = TRUE)), class = c("ci", "interval"), domain = "R", method = NA_character_, data = "Interval uses 32 data points from data mtcars$mpg with sample variance = 36.3241 and assumed kurtosis = 3.0000", confidence = 0.95, parameter = "mean for population of size 1000")
))
stopifnot(all.equal(
stat.extend::CONF.var(.05, mtcars$mpg, N=1000),
structure(list(list(l = 22.0730002460278, r = 57.9538604068809,
lc = TRUE, rc = TRUE)), class = c("ci", "interval"), domain = "R", method = "Computed using nlm optimisation with 7 iterations (code = 1)", data = "Interval uses 32 data points from data mtcars$mpg with sample variance = 36.3241 and sample kurtosis = 2.7995", confidence = 0.95, parameter = "variance for population of size 1000")
))
stopifnot(all.equal(
stat.extend::CONF.prop(.05, mtcars$mpg > 20, N=1000),
structure(list(list(l = 0.283745994666663, r = 0.604278989534686,
lc = TRUE, rc = TRUE)), class = c("ci", "interval"), domain = "R", data = "Interval uses 32 binary data points from data mtcars$mpg > 20 with sample proportion = 0.4375", method = NA_character_, confidence = 0.95, parameter = "proportion for population of size 1000")
))
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