% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sstn.R
\name{sstn}
\alias{sstn}
\title{Self-Similarity Test for Normality (SSTN)}
\usage{
sstn(x, verbose = FALSE)
}
\arguments{
\item{x}{Numeric vector of observations \eqn{(x_1, \dots, x_n)} drawn from
a distribution with finite variance. Must have length \eqn{n \ge 2}.}

\item{verbose}{Logical. If TRUE (default), prints a summary of the test results
including the number of summands, test statistic, and \eqn{p}-value.}
}
\value{
An invisible list with the following components:
\item{test.statistic}{Numeric. The observed value of the SSTN test statistic.}
\item{method}{Character string indicating whether the asymptotic or the finite-sample
null distribution was used.}
\item{p.value}{Numeric. The \eqn{p}-value of the test.}
}
\description{
The SSTN is a statistical test for assessing whether a given sample originates
from a normal distribution. It is based on the iterative application of the
empirical characteristic function and comparing these estimated characteristic functions.
A Monte Carlo procedure is used to obtain the empirical distribution of the test statistic
under the null hypothesis.
}
\examples{
set.seed(123)
# Sample from standard normal (null hypothesis true)
x <- rnorm(100)
res <- sstn(x)
res$p.value

# Sample from Gamma distribution (null hypothesis false)
y <- rgamma(100, 1)
res2 <- sstn(y)
res2$p.value
}
\references{
Anarat, A. and Schwender, H. (2026).
A test for normality based on self-similarity.
arXiv preprint \url{doi:10.48550/arXiv.2604.03810}.
}
\author{
Akin Anarat \email{akin.anarat@hhu.de}
}
