MCMC Chains improvements

In the last decades, Bayesian statistics has gained ground in the modelling of phenomena. Despite its advantages, to implement a Bayesian framework is still difficult and restricted because the available literature on Bayesian statistics generally focuses on mathematical formalism and requires greater statistical expertise than some other methods. For this reason, any tool or resource that facilitates the understanding of Bayesian statistics and its mathematical background will be very useful. Turing.jl is a high-performance probabilistic programming bayesian inference language inside Julia ecosystem. Inside Turing lang, MCMCChains.jl represents an implementation of Julia types for analyzing, storing and summarizing MCMC simulations and uses utility functions for diagnostics and results visualizations. This project comprises various tasks to improve different aspects of MCMCChains.jl such as plotting functionality and storage of MCMC sampling to achieve better heuristics.