Generalized Linear Mixed Models via Fully Exponential Laplace in EM // 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.