BayesLN: Bayesian Inference for Log-Normal Data

Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.

Version: 0.1.2
Depends: R (≥ 3.5.0)
Imports: optimx, ghyp, fAsianOptions, coda, Rcpp (≥ 0.12.17), MASS, lme4, data.table
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, RcppArmadillo
Published: 2020-02-20
Author: Aldo Gardini [aut, cre], Enrico Fabrizi [aut], Carlo Trivisano [aut]
Maintainer: Aldo Gardini <aldo.gardini2 at unibo.it>
License: GPL-3
NeedsCompilation: yes
CRAN checks: BayesLN results

Downloads:

Reference manual: BayesLN.pdf
Vignettes: Bayesian Inference with Log-normal Data
Package source: BayesLN_0.1.2.tar.gz
Windows binaries: r-prerelease: BayesLN_0.1.2.zip, r-release: BayesLN_0.1.2.zip, r-oldrel: BayesLN_0.1.2.zip
macOS binaries: r-prerelease: BayesLN_0.1.2.tgz, r-release: BayesLN_0.1.2.tgz, r-oldrel: BayesLN_0.1.2.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=BayesLN to link to this page.