Package: glmmFEL Type: Package Title: Generalized Linear Mixed Models via Fully Exponential Laplace in EM Version: 1.0.6 Date: 2026-09-18 Authors@R: person(given = "Andrew T.", family = "Karl", email = "akarl@asu.edu", role = c("cre", "aut"), comment = c(ORCID = "0000-0002-5933-8706")) Description: 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 variance-diagonal 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) , and is adapted from the implementation used in the 'RealVAMS' package (Broatch, Green, and Karl (2018)) . The FEL implementation follows Karl, Yang, and Lohr (2014) and related work (e.g., Tierney, Kass, and Kadane (1989) ; Rizopoulos, Verbeke, and Lesaffre (2009) ; Steele (1996) ). Package code was drafted with assistance from generative AI tools. License: GPL-3 Encoding: UTF-8 Imports: Matrix, stats, methods Suggests: testthat (>= 3.0.0), lme4, numDeriv Config/testthat/edition: 3 NeedsCompilation: no Config/roxygen2/version: 8.1.0 Packaged: 2026-10-18 11:17:55 UTC; root Author: Andrew T. Karl [cre, aut] (ORCID: ) Maintainer: Andrew T. Karl Repository: https://akarl46556.r-universe.dev Date/Publication: 2026-09-18 17:20:02 UTC RemoteUrl: https://github.com/cran/glmmFEL RemoteRef: HEAD RemoteSha: b829128caedd1c647bf543cfdcab93bec540fe45