RSGHB: Functions for Hierarchical Bayesian Estimation: A Flexible Approach
Version 1.1.2

Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html See Train's chapter on HB in Discrete Choice with Simulation here: http://elsa.berkeley.edu/books/choice2.html; and his paper on using HB with non-normal distributions here: http://eml.berkeley.edu//~train/trainsonnier.pdf.

AuthorJeff Dumont [aut, cre], Jeff Keller [aut], Chase Carpenter [ctb]
Date of publication2015-12-16 23:23:46
MaintainerJeff Dumont <Jeff.Dumont@rsginc.com>
LicenseGPL-3
Version1.1.2
URL https://github.com/RSGInc/RSGHB
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("RSGHB")

Popular man pages

choicedata: A synthetic discrete choice dataset
doHB: Estimate a Hierarchical Bayesian Model
plot: Plot RSGHB Model Object Components
writeModel: Write an RSGHB Model Object as Series of CSVs
See all...

All man pages Function index File listing

Man pages

choicedata: A synthetic discrete choice dataset
doHB: Estimate a Hierarchical Bayesian Model
plot: Plot RSGHB Model Object Components
writeModel: Write an RSGHB Model Object as Series of CSVs

Functions

checkModel Source code
choicedata Man page
doHB Man page Source code
hb Source code
nextA Source code
nextB Source code
nextD Source code
nextDind Source code
nextF Source code
onLoad Source code
plot Man page
plot.RSGHB Man page Source code
plotC Source code
prepareModel Source code
progreport Source code
trans Source code
writeModel Man page Source code

Files

inst
inst/doc
inst/doc/RSGHB_HowTo.pdf
inst/doc/RSGHB_HowTo.R
inst/doc/RSGHB_HowTo.rnw
src
src/RSGHB.c
NAMESPACE
NEWS
data
data/choicedata.txt.gz
R
R/plotC.R
R/hb.R
R/onLoad.R
R/nextA.R
R/prepareModel.R
R/methods.R
R/writeModel.R
R/doHB.R
R/nextD.R
R/nextF.R
R/checkModel.R
R/progreport.R
R/trans.R
R/nextB.R
R/nextDind.R
vignettes
vignettes/MNL_markovChains.png
vignettes/RSGHB_HowTo-concordance.tex
vignettes/MMNL_MarkovChains.png
vignettes/RSGHB_HowTo.rnw
MD5
build
build/vignette.rds
DESCRIPTION
man
man/writeModel.Rd
man/doHB.Rd
man/choicedata.Rd
man/plot.Rd
tools
tools/Distribution_Converter.R
RSGHB documentation built on May 19, 2017, 4:22 p.m.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.