Non-Negative Matrix Factorization with Kernel Covariates // 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.