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bayesestdft  

Estimating the Degrees of Freedom of the Student's t-Distribution under a Bayesian Framework
View on CRAN: Click here


Download and install bayesestdft package within the R console
Install from CRAN:
install.packages("bayesestdft")

Install from Github:
library("remotes")
install_github("cran/bayesestdft")

Install by package version:
library("remotes")
install_version("bayesestdft", "1.0.0")



Attach the package and use:
library("bayesestdft")
Maintained by
Somjit Roy
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-01-09
Latest Update: 2025-01-09
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.
How to cite:
Somjit Roy (2025). bayesestdft: Estimating the Degrees of Freedom of the Student's t-Distribution under a Bayesian Framework. R package version 1.0.0, https://cran.r-project.org/web/packages/bayesestdft. Accessed 26 Aug. 2026.
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