nmfkc: Non-Negative Matrix Factorization with Kernel Covariates (original) (raw)

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>.

Version:	0.8.2
Imports:	stats, graphics, utils, grDevices
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), mclust, palmerpenguins, quanteda, vars, DiagrammeR, MASS, nlme, lavaan
Published:	2026-06-14
DOI:	10.32614/CRAN.package.nmfkc
Author:	Kenichi Satoh [aut, cre]
Maintainer:	Kenichi Satoh
BugReports:	https://github.com/ksatohds/nmfkc/issues
License:	MIT + file
URL:	https://github.com/ksatohds/nmfkc,https://ksatohds.github.io/nmfkc/
NeedsCompilation:	no
Language:	en-US
Citation:	nmfkc citation info
Materials:	README, NEWS
CRAN checks:	nmfkc results

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