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BayesLN
View on CRAN: Click
here
Download and install BayesLN package within the R console
Install from CRAN:
install.packages("BayesLN")
Install from Github:
library("remotes")
install_github("cran/BayesLN") Install by package version:
library("remotes")
install_version("BayesLN", "0.2.12") Attach the package and use:
library("BayesLN")
Maintained by
Aldo Gardini
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-02-20
Latest Update: 2025-07-31
Description:
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 ). 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.
How to cite:
Aldo Gardini (2020). BayesLN: Bayesian Inference for Log-Normal Data. R package version 0.2.12, https://cran.r-project.org/web/packages/BayesLN. Accessed 25 Jun. 2026.
Previous versions and publish date:
Other packages that cited BayesLN R package
View BayesLN citation profile
Other R packages that BayesLN depends,
imports, suggests or enhances
Complete documentation for BayesLN
Functions, R codes and Examples using
the BayesLN R package
Some associated functions: EPA09 . GH_MGF . LN_Mean . LN_MeanReg . LN_Quant . LN_QuantReg . LN_hier_existence . LN_hierarchical . NCBC . RatioBesselK . ReadingTime . SMNGZmoment . SMNGdistribution . SMNGmoments . add . add_MGF . fatigue . functional_delta . functional_gamma . g_V . g_V_vec . inf_sum . inf_sum_MGF . integral . integral_MGF . laminators . momentRecursion . zerfun . zerfun_log .
Some associated R codes: Est_quant.R . Hier_model.R . LN_mean.R . RcppExports.R . data.R . prob_func.R . Full BayesLN package functions and examples
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