Estimating the Degrees of Freedom of the Student's t-Distribution under a Bayesian Framework R package bayesestdft version 1.0.0 A Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in <doi:10.3390/axioms11090462> to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior. Software Depends: R (>= 4.0.4) Imports: numDeriv, dplyr Somjit Roy <sroy_123@tamu.edu> Comprehensive R Archive Network (CRAN) Somjit Roy [aut, cre], Se Yoon Lee [aut, ctb] MIT + file LICENSE (https://CRAN.R-project.org/package=bayesestdft/LICENSE) 2025-01-09 application/tgz https://CRAN.R-project.org/package=bayesestdft doi:10.32614/CRAN.package.bayesestdft