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bayesdistreg
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Download and install bayesdistreg package within the R console
Install from CRAN:
install.packages("bayesdistreg")
Install from Github:
library("remotes")
install_github("cran/bayesdistreg") Install by package version:
library("remotes")
install_version("bayesdistreg", "0.1.0") Attach the package and use:
library("bayesdistreg")
Maintained by
Emmanuel Tsyawo
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-02-05
Latest Update: 2019-02-05
Description:
Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.
How to cite:
Emmanuel Tsyawo (2019). bayesdistreg: Bayesian Distribution Regression. R package version 0.1.0, https://cran.r-project.org/web/packages/bayesdistreg. Accessed 08 Jun. 2026.
Previous versions and publish date:
0.1.0 (2019-02-05 23:44)
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Complete documentation for bayesdistreg
Functions, R codes and Examples using
the bayesdistreg R package
Some associated functions: IndepMH . LogitLink . RWMH . asymcnfB . distreg.asymp . distreg . distreg.sas . distreg_cfa . distreg_cfa.sas . dr_asympar . fitdist . fitlogit . indicat . jdpar.asymp . jntCBOM . lapl_aprx . lapl_aprx2 . logit . parLply . par_distreg . posterior . prior_n . prior_u . quant_bdr . simcnfB .
Some associated R codes: Functions.R . Metropolis_Hastings_Algorithms.R . Regression_Functions.R . Full bayesdistreg package functions and examples
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