Metropolis algorithms have greatly expanded our ability to estimate parameter distributions. In this talk we introduce pymcmcstat [Miles, 2018], which utilizes the Delayed Rejection Adaptive Metropolis (DRAM) algorithm [Haario et al., 2006, Haario et al., 2001] to perform Markov Chain Monte Carlo (MCMC) simulations. The user interface provides a straight forward environment for experienced and new Python users to quickly compare their models with data. Furthermore, the package provides a wide variety of diagnostic tools for visualizing uncertainty propagation. This package has been utilized in a wide array of scientific and engineering problems, including radiation source localization and constitutive model development of smart material systems.