JarqueBeraTest(x, robust = TRUE, method = c("chisq", "mc"),
N = 0, na.rm = FALSE)
chisq
or mc
, specifying how the critical
values should be obtained. Default is approximated by the
chisq-distribution or empirically via Monte Carlo.MeanAD
) to estimate sample kurtosis and skewness. For more details see Gel and Gastwirth (2006).
Users can also choose to perform the classical Jarque-Bera test (see
Jarque, C. and Bera, A (1980)).shapiro.test
,
AndersonDarlingTest
, CramerVonMisesTest
, LillieTest
, PearsonTest
, ShapiroFranciaTest
qqnorm
, qqline
for producing a normal quantile-quantile plotx <- rnorm(100) # null hypothesis
JarqueBeraTest(x)
x <- runif(100) # alternative hypothesis
JarqueBeraTest(x, robust=TRUE)
Run the code above in your browser using DataCamp Workspace