  This paper introduces an extension to the normal distribution through the
polar method to capture bimodality and asymmetry, which are often observed
characteristics of empirical data. The later two features are entirely
controlled by a separate scalar parameter. Explicit expressions for the
cumulative distribution function, the density function and the moments were
derived. The stochastic representation of the distribution facilitates
implementing Bayesian estimation via the Markov chain Monte Carlo methods. Some
real-life data as well as simulated data are analyzed to illustrate the
flexibility of the distribution for modeling asymmetric bimodality.
