#
Logit_normal GLMM R Package

## The R Package

This web page is about an R package
(written by Yun Ju Sung and
Charles J. Geyer)
for doing Logit-Normal generalized linear
mixed models (GLMM) using ordinary, independent and identically distributed
Monte Carlo.

The package is now hosted at GitHub

https://github.com/cjgeyer/bernor

As the README at GitHub says, the package is easily installed using
R package `remotes`

(https://cran.r-project.org/package=remotes)

library(remotes)
install_github("cjgeyer/bernor", subdir = "package/bernor")

For more info see the package vignette
or the R help files for R functions `bnlogl`

and `bnbigw`

.
vignette("examples", "bernor")
library("bernor")
help("bnlogl")
help("bnbigw")

## A Paper About It

A paper about the theory used by this package and using the package for
examples is

Sung, Y. J. and Geyer, C. J. (2007).

Monte Carlo likelihood inference for missing data models.

*Annals of Statistics*, **35**, 990–1011.

doi:10.1214/009053606000001389

https://projecteuclid.org/euclid.aos/1185303995

Of no real interest except
that the proofs are longer and more detailed is
the first draft PDF.

## Supporting Materials for the Paper

Detailed verification of conditions of
the theorems in the paper
for the models done by the package.

A redo for the revision of the Booth and Hobert example
in the paper.

A new example for the revision from Coull and Agresti.

A redo for the revision of the salamander example,
also from Booth and Hobert, data originally
from McCullagh and Nelder.