Using MCMC chain outputs to efficiently estimate Bayes factors

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Simple relation between Bayesian order-restricted and point-null hypothesis tests (2014, methodology): “The two-sided Bayes factor BJZSe0 , calculated using the R package BayesFactor (using Gaussian quadrature; Morey and Rouder, 2014) and shown in the fourth column, indicates that the evidence in each replication attempt favors the null hypothesis that δ = 0 over an unrestricted alternative.”
Evaluating marginal likelihood with thermodynamic integration method and comparison with several other numerical methods (2016, methodology): “There have been other MC methods for evaluating the marginal likelihood, such as those using SavageDickey density ratio [Morey et al., 2011], Lebesgue integration theory [Weinberg, 2012], and stepping-stone method [Xie et al., 2011].”
Default Bayes factors for ANOVA designs (2012, methodology): “We develop a few nonlinear models, and compute the multiple-g prior Bayes factor using the Savage–Dickey density estimation approach (Dickey & Lientz, 1970; Morey et al., 2011).”