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glsm: Saturated Model Log-Likelihood for Multinomial Outcomes

When the response variable Y takes one of R > 1 values, the function 'glsm()' computes the maximum likelihood estimates (MLEs) of the parameters under four models: null, complete, saturated, and logistic. It also calculates the log-likelihood values for each model. This method assumes independent, non-identically distributed variables. For grouped data with a multinomial outcome, where observations are divided into J populations, the function 'glsm()' provides estimation for any number K of explanatory variables.

Version: 0.0.0.6
Depends: R (≥ 3.5.0)
Imports: stats, dplyr (≥ 1.0.0), ggplot2 (≥ 1.0.0), VGAM (≥ 1.0.0), plyr
Published: 2025-07-14
DOI: 10.32614/CRAN.package.glsm
Author: Jorge Villalba ORCID iD [aut, cre], Humberto Llinas ORCID iD [aut], Jorge Borja ORCID iD [aut], Jorge Tilano ORCID iD [aut]
Maintainer: Jorge Villalba <jvillalba at utb.edu.co>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: glsm citation info
Materials: README
CRAN checks: glsm results

Documentation:

Reference manual: glsm.pdf

Downloads:

Package source: glsm_0.0.0.6.tar.gz
Windows binaries: r-devel: glsm_0.0.0.6.zip, r-release: glsm_0.0.0.6.zip, r-oldrel: glsm_0.0.0.6.zip
macOS binaries: r-release (arm64): glsm_0.0.0.6.tgz, r-oldrel (arm64): glsm_0.0.0.6.tgz, r-release (x86_64): glsm_0.0.0.6.tgz, r-oldrel (x86_64): glsm_0.0.0.6.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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