Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent R package sgdGMF version 1.0.1 Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, <doi:10.48550/arXiv.2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, <http://jmlr.org/papers/v23/20-1104.html>). Software Depends: R (>= 4.0.0), ggplot2 Imports: Rcpp (>= 1.0.10), RcppArmadillo, RSpectra, parallel, doParallel, foreach, MASS, SuppDists, methods, generics, reshape2, ggpubr, viridisLite LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), Rtsne, dplyr, knitr, rmarkdown Cristian Castiglione <cristian_castiglione@libero.it> Comprehensive R Archive Network (CRAN) Cristian Castiglione [aut, cre] (ORCID: <https://orcid.org/0000-0001-5883-4890>), Davide Risso [ctb] (ORCID: <https://orcid.org/0000-0001-8508-5012>), Alexandre Segers [ctb] (ORCID: <https://orcid.org/0009-0004-2028-7595>) MIT + file LICENSE (https://CRAN.R-project.org/package=sgdGMF/LICENSE) 2025-05-17 application/tgz https://CRAN.R-project.org/package=sgdGMF doi:10.32614/CRAN.package.sgdGMF