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glmm  

Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("glmm", "1.4.4")



Attach the package and use:
library("glmm")
Maintained by
Christina Knudson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-02-12
Latest Update: 2022-10-09
Description:
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
How to cite:
Christina Knudson (2015). glmm: Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation. R package version 1.4.4, https://cran.r-project.org/web/packages/glmm
Previous versions and publish date:
1.0.1 (2015-02-12 07:42), 1.0.2 (2015-03-20 00:59), 1.0.3 (2015-04-29 01:48), 1.0.4 (2015-07-24 19:05), 1.0 (2015-02-11 07:46), 1.1.0 (2016-08-13 18:15), 1.1.1 (2016-08-18 00:28), 1.2.1 (2017-08-16 05:19), 1.2.2 (2017-11-07 17:09), 1.2.3 (2018-02-22 18:32), 1.2 (2017-07-25 18:46), 1.3.0 (2018-12-12 00:20), 1.4.2 (2020-06-21 15:40), 1.4.3 (2022-04-07 09:42), 1.4 (2020-05-31 12:30)
Other packages that cited glmm R package
View glmm citation profile
Other R packages that glmm depends, imports, suggests or enhances
Functions, R codes and Examples using the glmm R package
Some associated functions: Booth2 . BoothHobert . bacteria . bernoulli.glmm . binomial.glmm . cbpp2 . coef.glmm . confint.glmm . glmm . logLik . mcse . mcvcov . murder . poisson.glmm . radish2 . salamander . se . summary.glmm . varcomps.glmm . vcov.glmm . 
Some associated R codes: addMats.R . distRand.R . genRand.R . getEk.R . getFamily.R . glmm.R . mcse.R . objfun.R . pql.R . summary.mcla.R .  Full glmm package functions and examples
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