Package: glsm
Type: Package
Title: Saturated Model Log-Likelihood for Multinomial Outcomes
Version: 0.0.0.6
Date: 2025-07-09
Authors@R: c(
  person("Jorge", "Villalba", email = "jvillalba@utb.edu.co", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2888-9660")),
  person("Humberto", "Llinas", email = "hllinas@uninorte.edu.co", role = c("aut"), comment = c(ORCID = "0000-0002-2976-5109")),
  person("Jorge", "Borja", email = "jborjaa@uninorte.edu.co", role = "aut", comment = c(ORCID = "0009-0006-4824-5199")),
  person("Jorge", "Tilano", email = "jtilano@uninorte.edu.co", role = "aut", comment = c(ORCID = "0009-0005-5793-4183"))
  )
Author: Jorge Villalba [aut, cre] (ORCID:
        <https://orcid.org/0000-0002-2888-9660>), Humberto Llinas [aut]
        (ORCID: <https://orcid.org/0000-0002-2976-5109>), Jorge Borja
        [aut] (ORCID: <https://orcid.org/0009-0006-4824-5199>), Jorge
        Tilano [aut] (ORCID: <https://orcid.org/0009-0005-5793-4183>)
Maintainer: Jorge Villalba <jvillalba@utb.edu.co>
Description: 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.
Depends: R (>= 3.5.0)
Imports: stats, dplyr (>= 1.0.0), ggplot2 (>= 1.0.0), VGAM (>= 1.0.0),
        plyr
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
LazyData: true
NeedsCompilation: no
Packaged: 2026-06-18 08:56:47 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2025-07-14 17:40:39 UTC
RemoteUrl: https://github.com/cran/glsm
RemoteRef: HEAD
RemoteSha: b8569e9f4a2c7446dcd1799ca901d0533dfed8da