We provide a copula-based direct utility approach to incorporate dependencies between unobserved shocks of choice alternatives in modeling multiple-discrete/continuous choice outcomes with a closed form likelihood function. The dependencies which inevitably occurs in the multiple choice outcomes have been widely investigated in the multi-category literature. Handling the dependencies under the multiple-discrete/continuous choice context usually requires multiple integrals in calculating the likelihood function. However, the multiple integrals are analytically intractable with correlated choice alternatives. Hence, the choice alternatives are often assumed to be independent for feasible analytical derivation of the multiple integrals. Our copula-based approach replaces the multiple integrals with multiple differentiations using copula. Thus, it can handle the multiple-discrete/continuous choice outcomes with correlated choice alternatives with a simple closed form likelihood function. We apply the proposed model to IRI scanner panel data and estimate the proposed model using a Bayesian MCMC method. We find strong empirical evidence of the dependencies among unobserved shocks of choice alternatives. Also, we show that overlooking the dependencies might bias managerial metrics such as joint-purchase demand. Moreover, we find that ignoring the dependencies results in under/over-estimation of the utility-compensating price reductions for bundling promotions which might, in turn, harm the profitability of bundling promotions.