  Many generalised distributions exist for modelling data with vastly diverse
characteristics. However, very few of these generalisations of the normal
distribution have shape parameters with clear roles that determine, for
instance, skewness and tail shape. In this chapter, we review existing skewing
mechanisms and their properties in detail. Using the knowledge acquired, we add
a skewness parameter to the body-tail generalised normal distribution
\cite{BTGN}, that yields the \ac{FIN} with parameters for location, scale,
body-shape, skewness, and tail weight. Basic statistical properties of the
\ac{FIN} are provided, such as the \ac{PDF}, cumulative distribution function,
moments, and likelihood equations. Additionally, the \ac{FIN} \ac{PDF} is
extended to a multivariate setting using a student t-copula, yielding the
\ac{MFIN}. The \ac{MFIN} is applied to stock returns data, where it outperforms
the t-copula multivariate generalised hyperbolic, Azzalini skew-t, hyperbolic,
and normal inverse Gaussian distributions.
