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eMLEloglin  

Fitting log-Linear Models in Sparse Contingency Tables
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("eMLEloglin", "1.0.1")



Attach the package and use:
library("eMLEloglin")
Maintained by
Matthew Friedlander
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-11-24
Latest Update:
Description:
Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse the data can fall on the boundary of the convex support and we say that the MLE does not exist in the sense that some parameters cannot be estimated. However an extended MLE always exists and a subset of the original parameters will be estimable. The eMLEloglin package determines which sampling zeros contribute to the non-existence of the MLE. These problematic zero cells can be removed from the contingency table and the model can then be fit as far as is possible using the glm function.
How to cite:
Matthew Friedlander (2016). eMLEloglin: Fitting log-Linear Models in Sparse Contingency Tables. R package version 1.0.1, https://cran.r-project.org/web/packages/eMLEloglin. Accessed 05 Mar. 2026.
Previous versions and publish date:
1.0.1 (2016-11-30 18:31), 1.0 (2016-11-24 00:36)
Other packages that cited eMLEloglin R package
View eMLEloglin citation profile
Other R packages that eMLEloglin depends, imports, suggests or enhances
Functions, R codes and Examples using the eMLEloglin R package
Some associated functions: eMLEloglin . facial_set . rochdale . 
Some associated R codes: facial_set.R .  Full eMLEloglin package functions and examples
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