Non-Negative Matrix Factorization with Kernel Covariates R package nmfkc version 0.6.7 Performs Non-negative Matrix Factorization (NMF) with Kernel Covariates. Given an observation matrix and kernel covariates, it optimizes both a basis matrix and a parameter matrix. Notably, if the kernel matrix is an identity matrix, the method simplifies to standard NMF. Also provides NMF with Random Effects (NMF-RE) via nmfre(), which estimates a mixed-effects model combining covariate-driven scores with unit-specific random effects together with wild bootstrap inference, and NMF-based Structural Equation Modeling (NMF-SEM) via nmf.sem(), which fits a two-block input-output model for blind source separation and path analysis. References: Satoh (2025) <doi:10.48550/arXiv.2403.05359>; Satoh (2025) <doi:10.48550/arXiv.2510.10375>; Satoh (2025) <doi:10.48550/arXiv.2512.18250>; Satoh (2026) <doi:10.48550/arXiv.2603.01468>; Satoh (2026) <doi:10.1007/s42081-025-00314-0>. Software Imports: stats, graphics, utils, grDevices Suggests: knitr, rmarkdown, testthat (>= 3.0.0), mclust, palmerpenguins, quanteda, vars, DiagrammeR, MASS, nlme, lavaan Kenichi Satoh <kenichi-satoh@biwako.shiga-u.ac.jp> Comprehensive R Archive Network (CRAN) Kenichi Satoh [aut, cre] (ORCID: <https://orcid.org/0000-0003-4436-9347>) MIT + file LICENSE (https://CRAN.R-project.org/package=nmfkc/LICENSE) 2026-04-15 application/tgz https://CRAN.R-project.org/package=nmfkc doi:10.32614/CRAN.package.nmfkc en-US