Package: bayesestdft
Title: Estimating the Degrees of Freedom of the Student's
        t-Distribution under a Bayesian Framework
Version: 1.0.0
Authors@R: 
    c(person(given = "Somjit", 
    family = "Roy",
    email = "sroy_123@tamu.edu", 
    role = c("aut", "cre")),
    person(given = c("Se", "Yoon"), 
    family = "Lee",
    email = "seyoonlee.stat.math@gmail.com", 
    role = c("aut", "ctb")))
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
URL: https://github.com/Roy-SR-007/bayesestdft
BugReports: https://github.com/Roy-SR-007/bayesestdft/issues
Imports: numDeriv, dplyr
Depends: R (>= 4.0.4)
LazyData: true
NeedsCompilation: no
Packaged: 2025-01-09 05:01:34 UTC; somjit
Author: Somjit Roy [aut, cre],
  Se Yoon Lee [aut, ctb]
Maintainer: Somjit Roy <sroy_123@tamu.edu>
Repository: CRAN
Date/Publication: 2025-01-09 18:10:01 UTC
Built: R 4.6.0; ; 2026-04-28 23:38:17 UTC; unix
