Generalized Linear Mixed Models via Fully Exponential Laplace in EM R package glmmFEL version 1.0.5 Fit generalized linear mixed models (GLMMs) with normal random effects using first-order Laplace, fully exponential Laplace (FEL) with mean-only corrections, and FEL with mean and covariance corrections in the E-step of an expectation-maximization (EM) algorithm. The current development version provides a matrix-based interface (y, X, Z) and supports binary logit and probit, and Poisson log-link models. An EM framework is used to update fixed effects, random effects, and a single variance component tau^2 for G = tau^2 I, with staged approximations (Laplace -> FEL mean-only -> FEL full) for efficiency and stability. A pseudo-likelihood engine glmmFEL_pl() implements the working-response / working-weights linearization approach of Wolfinger and O'Connell (1993) <doi:10.1080/00949659308811554>, and is adapted from the implementation used in the 'RealVAMS' package (Broatch, Green, and Karl (2018)) <doi:10.32614/RJ-2018-033>. The FEL implementation follows Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019> and related work (e.g., Tierney, Kass, and Kadane (1989) <doi:10.1080/01621459.1989.10478824>; Rizopoulos, Verbeke, and Lesaffre (2009) <doi:10.1111/j.1467-9868.2008.00704.x>; Steele (1996) <doi:10.2307/2532845>). Package code was drafted with assistance from generative AI tools. Software Imports: Matrix, numDeriv, stats, methods Suggests: testthat (>= 3.0.0), MASS, knitr, rmarkdown, nlme, mvglmmRank, lme4 Andrew T. Karl <akarl@asu.edu> Comprehensive R Archive Network (CRAN) Andrew T. Karl [cre, aut] (ORCID: <https://orcid.org/0000-0002-5933-8706>) GPL-3 2026-01-09 application/tgz https://CRAN.R-project.org/package=glmmFEL doi:10.32614/CRAN.package.glmmFEL