{ "_id": "6a101060acfb0bcc41c81de9", "Package": "M2SMJF", "Title": "Multi-Modal Similarity Matrix Joint Factorization", "Version": "1.0", "Authors@R": "person(\"Xiaoyao\", \"Yin\", email = \"yinxy1992@sina.com\", role = c(\"aut\", \"cre\"))", "Description": "A new method to implement clustering from multiple\nmodality data of certain samples, the function M2SMjF() jointly\nfactorizes multiple similarity matrices into a shared\nsub-matrix and several modality private sub-matrices, which is\nfurther used for clustering. Along with this method, we also\nprovide function to calculate the similarity matrix and\nfunction to evaluate the best cluster number from the original\ndata.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "RoxygenNote": "7.1.1", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-22 08:11:35 UTC", "User": "root" }, "Author": "Xiaoyao Yin [aut, cre]", "Maintainer": "Xiaoyao Yin ", "Repository": "https://yinxy1992.r-universe.dev", "Date/Publication": "2020-11-23 07:40:06 UTC", "RemoteUrl": "https://github.com/cran/M2SMJF", "RemoteRef": "HEAD", "RemoteSha": "3d54f69f3524716ed2d7c767ab00c3efdababf4d", "MD5sum": "2394a0a28928e1f3466a54823892d4a0", "_user": "yinxy1992", "_type": "src", "_file": "M2SMJF_1.0.tar.gz", "_fileid": "6cce0ecc81d41b8fc58680ac0aaca7b9647410061b046ac1f41c4e4d8c10e0a8", "_filesize": 114765, "_sha256": "6cce0ecc81d41b8fc58680ac0aaca7b9647410061b046ac1f41c4e4d8c10e0a8", "_created": "2026-05-22T08:11:35.000Z", "_published": 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normalized mutual information.", "topics": [ "Cal_NMI" ] }, { "page": "cost", "title": "Calculate the cost", "topics": [ "cost" ] }, { "page": "dist2bin", "title": "Calculate the agreement-based measurement", "topics": [ "dist2bin" ] }, { "page": "dist2chi", "title": "Calculate the chi-squared distance", "topics": [ "dist2chi" ] }, { "page": "dist2eu", "title": "Calculate the Euclidean distance", "topics": [ "dist2eu" ] }, { "page": "initialization", "title": "initialize the sub-matrix Ci into alpha*Li by SVD", "topics": [ "initialization" ] }, { "page": "initialize_WL", "title": "Initialize from the similairty matrix list", "topics": [ "initialize_WL" ] }, { "page": "M2SMJF", "title": "the main part for M2SMJF and clustering result", "topics": [ "M2SMJF" ] }, { "page": "new_modularity", "title": "Calculate the modularity", "topics": [ "new_modularity" ] }, { "page": "simu_data_gen", "title": "Generate simulated data", "topics": [ "simu_data_gen" ] }, { "page": "Standard_Normalization", "title": "Normalize the input matrix by column", "topics": [ "Standard_Normalization" ] }, { "page": "update_alpha", "title": "the function to update alpha", "topics": [ "update_alpha" ] }, { "page": "update_L", "title": "the function to update Li, for i=1,2,...,N", "topics": [ "update_L" ] } ], "_rundeps": [ "cli", "dplyr", "generics", "glue", "lifecycle", "magrittr", "MASS", "pillar", "pkgconfig", "R6", "rlang", "tibble", "tidyselect", "utf8", "vctrs", "withr" ], "_vignettes": [ { "source": "introduction.Rmd", "filename": "introduction.html", "title": "An Introduction to the package M2SMJF", "author": "Xiaoyao Yin", "engine": "knitr::rmarkdown", "headings": [ "Installation", "Usage", "Simulation data generation", "Simulation data groundtruth assignment and permutation", "You should start from here if you are using your own data.", "You can ommit the following if you do not have any true label as the groudtruth, we do the next to evaluate our method." ], 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