  We develop two novel approaches for constructing skewed and bimodal flexible
distributions that can effectively generalize classical symmetric
distributions. We illustrate the application of introduced techniques by
extending normal, student-t, and Laplace distributions. We also study the
properties of the newly constructed distributions. The method of maximum
likelihood is proposed for estimating the model parameters. Furthermore, the
application of new distributions is represented using real-life data.
