{ "_id": "6a1022e6acfb0bcc41c8d3ac", "Package": "nmfkc", "Type": "Package", "Title": "Non-Negative Matrix Factorization with Kernel Covariates", "Version": "0.7.3", "Date": "2026-05-14", "Authors@R": "person(\"Kenichi\", \"Satoh\",\nemail = \"kenichi-satoh@biwako.shiga-u.ac.jp\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-4436-9347\"))", "Maintainer": "Kenichi Satoh ", "URL": "https://github.com/ksatohds/nmfkc,\nhttps://ksatohds.github.io/nmfkc/", "BugReports": "https://github.com/ksatohds/nmfkc/issues", "Description": "Performs Non-negative Matrix Factorization (NMF) with\nKernel Covariates. Given an observation matrix and kernel\ncovariates, it optimizes both a basis matrix and a parameter\nmatrix. Notably, if the kernel matrix is an identity matrix,\nthe method simplifies to standard NMF. Also provides NMF with\nRandom Effects (NMF-RE) via nmfre(), which estimates a\nmixed-effects model combining covariate-driven scores with\nunit-specific random effects together with wild bootstrap\ninference, and NMF-based Structural Equation Modeling (NMF-SEM)\nvia nmf.sem(), which fits a two-block input-output model for\nblind source separation and path analysis. References: Satoh\n(2025) ; Satoh (2025)\n; Satoh (2025)\n; Satoh (2026)\n; Satoh (2026)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Language": "en-US", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.2.3", "ByteCompile": "true", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Repository": "https://ksatohds.r-universe.dev", "Date/Publication": "2026-05-13 22:54:06 UTC", "RemoteUrl": "https://github.com/ksatohds/nmfkc", "RemoteRef": "HEAD", "RemoteSha": "c6803b3d31b830e78b2898b15bd7f63dd1fe9ed4", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-13 23:16:00 UTC", "User": "root" }, "Author": "Kenichi Satoh [aut, cre] (ORCID:\n)", "MD5sum": "37cbb39bd1d8bd1b93919a0d03dd16be", "_user": "ksatohds", "_type": "src", "_file": "nmfkc_0.7.3.tar.gz", "_fileid": "428feded22db74728364466088363d3ae056e27d4d579c71b180e5c486f9dc93", "_filesize": 1844290, "_sha256": "428feded22db74728364466088363d3ae056e27d4d579c71b180e5c486f9dc93", "_created": 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[ "nmfae.signed.inference" ] }, { "page": "nmfae.signed.rename", "title": "Rename Dec/Enc labels on nmfae.signed objects", "topics": [ "nmfae.signed.rename" ] }, { "page": "nmfkc", "title": "Optimize NMF with kernel covariates (Full Support for Missing Values)", "topics": [ "nmfkc" ] }, { "page": "nmfkc.ar", "title": "Construct observation and covariate matrices for a vector autoregressive model", "topics": [ "nmfkc.ar" ] }, { "page": "nmfkc.ar.degree.cv", "title": "Optimize lag order for the autoregressive model", "topics": [ "nmfkc.ar.degree.cv" ] }, { "page": "nmfkc.ar.DOT", "title": "Generate a Graphviz DOT Diagram for NMF-AR / NMF-VAR Models", "topics": [ "nmfkc.ar.DOT" ] }, { "page": "nmfkc.ar.predict", "title": "Forecast future values for NMF-VAR model", "topics": [ "nmfkc.ar.predict" ] }, { "page": "nmfkc.ar.stationarity", "title": "Check stationarity of an NMF-VAR model", "topics": [ "nmfkc.ar.stationarity" ] }, { "page": "nmfkc.class", "title": "Create a class (one-hot) matrix from a categorical vector", "topics": [ "nmfkc.class" ] }, { "page": "nmfkc.criterion", "title": "Compute model selection criteria for a fitted nmfkc model", "topics": [ "nmfkc.criterion" ] }, { "page": "nmfkc.cv", "title": "Perform k-fold cross-validation for NMF with kernel covariates", "topics": [ "nmfkc.cv" ] }, { "page": "nmfkc.denormalize", "title": "Denormalize a matrix from [0,1] back to its original scale", "topics": [ "nmfkc.denormalize" ] }, { "page": "nmfkc.DOT", "title": "Generate Graphviz DOT Scripts for NMF or NMF-with-Covariates Models", "topics": [ "nmfkc.DOT" ] }, { "page": "nmfkc.ecv", "title": "Perform Element-wise Cross-Validation (Wold's CV)", "topics": [ "nmfkc.ecv" ] }, { "page": "nmfkc.inference", "title": "Statistical inference for the parameter matrix C (Theta)", "topics": [ "nmfkc.inference" ] }, { "page": "nmfkc.kernel", "title": "Create a kernel matrix from covariates", "topics": [ "nmfkc.kernel" ] }, { "page": "nmfkc.kernel.beta.cv", "title": 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matrix to the range [0,1]", "topics": [ "nmfkc.normalize" ] }, { "page": "nmfkc.rank", "title": "Rank selection diagnostics with graphical output", "topics": [ "nmfkc.rank" ] }, { "page": "nmfkc.residual.plot", "title": "Plot Diagnostics: Original, Fitted, and Residual Matrices as Heatmaps", "topics": [ "nmfkc.residual.plot" ] }, { "page": "nmfkc.signed", "title": "NMF-KC with signed covariate matrix", "topics": [ "nmfkc.signed" ] }, { "page": "nmfkc.signed.cv", "title": "Column-wise k-fold cross-validation for nmfkc.signed", "topics": [ "nmfkc.signed.cv" ] }, { "page": "nmfkc.signed.ecv", "title": "Element-wise cross-validation for nmfkc.signed", "topics": [ "nmfkc.signed.ecv" ] }, { "page": "nmfre", "title": "Non-negative Matrix Factorization with Random Effects", "topics": [ "nmfre" ] }, { "page": "nmfre.dfU.scan", "title": "Scan dfU cap rates for NMF-RE", "topics": [ "nmfre.dfU.scan" ] }, { "page": "nmfre.inference", "title": "Statistical inference for the coefficient matrix C from NMF-RE", "topics": [ "nmfre.inference" ] }, { "page": "plot.nmfae", "title": "'plot.nmfae' displays the convergence trajectory of the objective function across iterations. The title shows the achieved R^2.", "topics": [ "plot.nmfae" ] }, { "page": "plot.nmfae.cv", "title": "Plot method for nmfae.cv objects", "topics": [ "plot.nmfae.cv" ] }, { "page": "plot.nmfae.ecv", "title": "Plot method for nmfae.ecv objects", "topics": [ "plot.nmfae.ecv" ] }, { "page": "plot.nmfae.kernel.beta.cv", "title": "Plot method for nmfae.kernel.beta.cv objects", "topics": [ "plot.nmfae.kernel.beta.cv" ] }, { "page": "plot.nmfae.signed", "title": "Plot method for nmfae.signed (convergence)", "topics": [ "plot.nmfae.signed" ] }, { "page": "plot.nmfkc", "title": "Plot method for objects of class 'nmfkc'", "topics": [ "plot.nmfkc" ] }, { "page": "plot.nmfkc.DOT", "title": "Plot method for nmfkc.DOT objects", "topics": [ "plot.nmfkc.DOT" ] }, { "page": "plot.nmfkc.signed", "title": "Plot method for nmfkc.signed (convergence)", "topics": [ "plot.nmfkc.signed" ] }, { "page": "plot.nmfre", "title": "Plot convergence diagnostics for NMF models", "topics": [ "plot.nmf.sem", "plot.nmfre" ] }, { "page": "plot.predict.nmfae", "title": "Plot method for predict.nmfae objects", "topics": [ "plot.predict.nmfae" ] }, { "page": "predict.nmfae", "title": "Predict method for nmfae objects", "topics": [ "predict.nmfae" ] }, { "page": "predict.nmfae.signed", "title": "Predict method for nmfae.signed", "topics": [ "predict.nmfae.signed" ] }, { "page": "predict.nmfkc", "title": "Prediction method for objects of class 'nmfkc'", "topics": [ "predict.nmfkc" ] }, { "page": "predict.nmfkc.signed", "title": "Predict method for nmfkc.signed", "topics": [ "predict.nmfkc.signed" ] }, { "page": "print.nmf.inference", "title": "Print method for NMF inference objects", "topics": [ "print.nmf.inference" ] }, { "page": "print.summary.nmfae", "title": "Print method for summary.nmfae objects", "topics": [ "print.summary.nmfae" ] }, { "page": "print.summary.nmfae.inference", "title": "Print method for summary.nmfae.inference objects", "topics": [ "print.summary.nmfae.inference" ] }, { "page": "print.summary.nmfae.signed", "title": "Print method for summary.nmfae.signed", "topics": [ "print.summary.nmfae.signed" ] }, { "page": "print.summary.nmfae.signed.inference", "title": "Print method for summary.nmfae.signed.inference objects", "topics": [ "print.summary.nmfae.signed.inference" ] }, { "page": "print.summary.nmfkc", "title": "Print method for 'summary.nmfkc' objects", "topics": [ "print.summary.nmfkc" ] }, { "page": "print.summary.nmfkc.inference", "title": "Print method for summary.nmfkc.inference objects", "topics": [ "print.summary.nmfkc.inference" ] }, { "page": "print.summary.nmfkc.net", "title": "Print method for summary.nmfkc.net objects", "topics": [ "print.summary.nmfkc.net" ] }, { "page": "print.summary.nmfkc.net.inference", "title": "Print method for summary.nmfkc.net.inference objects", "topics": [ "print.summary.nmfkc.net.inference" ] }, { "page": "print.summary.nmfkc.net.signed", "title": "Print method for summary.nmfkc.net.signed objects", "topics": [ "print.summary.nmfkc.net.signed" ] }, { "page": "print.summary.nmfkc.signed", "title": "Print method for summary.nmfkc.signed", "topics": [ "print.summary.nmfkc.signed" ] }, { "page": "residuals.nmf", "title": "Extract residuals from NMF models", "topics": [ "residuals.nmf", "residuals.nmf.sem", "residuals.nmfae", "residuals.nmfre" ] }, { "page": "summary.nmf.sem", "title": "Summary method for nmf.sem objects", "topics": [ "summary.nmf.sem" ] }, { "page": "summary.nmfae", "title": "Summary method for nmfae objects", "topics": [ "summary.nmfae" ] }, { "page": "summary.nmfae.inference", "title": "Summary method for nmfae.inference objects", "topics": [ "summary.nmfae.inference" ] }, { "page": "summary.nmfae.signed", "title": "Summary method for nmfae.signed", "topics": [ "summary.nmfae.signed" ] }, { "page": "summary.nmfae.signed.inference", "title": "Summary method for nmfae.signed.inference objects", "topics": [ "summary.nmfae.signed.inference" ] }, { "page": 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