Package: pleLMA
Type: Package
Title: Pseudo-Likelihood Estimation of Log-Multiplicative Association
        Models
Version: 0.1.0
Author: Carolyn J. Anderson
Maintainer: Carolyn J. Anderson <cja@illinois.edu>
Description: Log-multiplicative association models (LMA) are
    models for cross-classifications of categorical variables 
    where interactions are represented by products of category 
    scale values and an association parameter. Maximum 
    likelihood estimation (MLE) fails for moderate to large 
    numbers of categorical variables. The 'pleLMA' package     
    overcomes this limitation of MLE by using pseudo-likelihood 
    estimation to fit the models to small or large 
    cross-classifications dichotomous or multi-category variables. 
    Originally proposed by Besag (1974, 
    <doi:10.1111/j.2517-6161.1974.tb00999.x>), pseudo-likelihood 
    estimation takes large complex models and breaks it down 
    into smaller ones. Rather than maximizing the likelihood 
    of the joint distribution of all the variables, a 
    pseudo-likelihood function, which is the product likelihoods
    from conditional distributions, is maximized. LMA models can 
    be derived from a number of different frameworks including 
    (but not limited to) graphical models and uni-dimensional 
    and multi-dimensional item response theory models. More 
    details about the models and estimation can be found in 
    the vignette.       
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: mnlogit, stats, graphics
Suggests: ggplot2, knitr, rmarkdown, testthat
RoxygenNote: 7.1.1
Depends: R (>= 2.10)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-04-26 16:48:57 UTC; cja
Repository: CRAN
Date/Publication: 2021-04-27 08:10:02 UTC
