## RSGHB: Functions for Hierarchical Bayesian Estimation: A Flexible
Approach

This package can be used to estimate models using a
hierarchical Bayesian 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 log-normal, negative
log-normal, positive truncated 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://elsa.berkeley.edu/~train/trainsonnier.pdf

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