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glsm  

Saturated Model Log-Likelihood for Multinomial Outcomes
View on CRAN: Click here


Download and install glsm package within the R console
Install from CRAN:
install.packages("glsm")

Install from Github:
library("remotes")
install_github("cran/glsm")

Install by package version:
library("remotes")
install_version("glsm", "0.0.0.6")



Attach the package and use:
library("glsm")
Maintained by
Jorge Villalba
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-07-14
Latest Update: 2025-07-14
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.
How to cite:
Jorge Villalba (2025). glsm: Saturated Model Log-Likelihood for Multinomial Outcomes. R package version 0.0.0.6, https://cran.r-project.org/web/packages/glsm. Accessed 04 Oct. 2026.
Previous versions and publish date:
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Complete documentation for glsm
Functions, R codes and Examples using the glsm R package
Full glsm package functions and examples
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