nmfkc: 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 with feed-forward and feedback structure (NMF-FFB) via nmf.ffb(), 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 (2026) <doi:10.1007/s42081-026-00349-x>; Satoh (2025) <doi:10.48550/arXiv.2512.18250>; Satoh (2026) <doi:10.48550/arXiv.2603.01468>; Satoh and Tokuda (2026) <doi:10.48550/arXiv.2607.27474>; Satoh (2026) <doi:10.1007/s42081-025-00314-0>.

install.packages("nmfkc")

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