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ABSTRACT:  This paper explores a way to construct a new family of univariate probability distributions where the parameters of the distribution capture the dependence between the variable of interest and the continuous latent state variable (the regime). The distribution nests well-known families of distributions, namely the skew normal family of Azzalini (1985) and a mixture of two Arnold et al. (2000) distributions when the mixing probability is a free parameter. We provide a stochastic representation of the distribution which enables the user to easily simulate the data from the underlying distribution using generated uniform and normal variates. We also derive the moment generating function and the moments. The distribution comprises eight free parameters that make it very flexible. This flexibility allows the user to capture many stylized facts about the data such as regime dependence, asymmetry, and fat tails as well as thin tails.