The chapter acknowledges support from various institutions and individuals, including the United States National Science Foundation, Washington University, and Harvard University. It also provides a bibliography of key references and introduces the CODA package, which is designed for convergence diagnosis and output analysis in Markov Chain Monte Carlo (MCMC) simulations. The CODA package includes functions for visualizing MCMC output, performing convergence diagnostics, and calculating summary statistics. The chapter discusses the history of CODA, its object-based infrastructure, and the process of reading MCMC data into R. It also covers the use of graphical and formal methods for convergence testing and highlights the need for better integration between MCMC engines and R to improve Bayesian data analysis practices. Finally, it acknowledges contributions from several individuals and provides a bibliography of related works.The chapter acknowledges support from various institutions and individuals, including the United States National Science Foundation, Washington University, and Harvard University. It also provides a bibliography of key references and introduces the CODA package, which is designed for convergence diagnosis and output analysis in Markov Chain Monte Carlo (MCMC) simulations. The CODA package includes functions for visualizing MCMC output, performing convergence diagnostics, and calculating summary statistics. The chapter discusses the history of CODA, its object-based infrastructure, and the process of reading MCMC data into R. It also covers the use of graphical and formal methods for convergence testing and highlights the need for better integration between MCMC engines and R to improve Bayesian data analysis practices. Finally, it acknowledges contributions from several individuals and provides a bibliography of related works.