Monte Carlo simulation is the most popular technique for performing uncertainty quantification for being easy to implement and requiring very few assumption on the behavior of the model. However, in cases where model evaluations are computational costly, the technique can be become too expensive since Monte Carlo requires a high number of evaluations to get reasonable accuracy. To mitigate this cost, various variance reduction techniques have been introduced to increase the convergence rate. Unfortunately these techniques are only just making in-roads into computational modelling because of their inherent complexity and interdependence. RoseDist is a software toolbox in Python designed to make most variance reduction technique accessible, in an object-oriented sense, to numerical modellers from various disciplines.