{ "_id": "6a15e64aacfb0bcc41d6d015", "Package": "MMAD", "Title": "Minorization-Maximization via Assembly-Decomposition Technology", "Version": "3.0.0", "Authors@R": "c(person(given = \"Xifen\", family = \"Huang\", role = \"aut\", email = \"xf_yellow@126.com\"),\nperson(given = \"Jinfeng\", family = \"Xu\", role = \"aut\", email = \"jinfenxu@cityu.edu.hk\"),\nperson(given = \"Jiaqi\", family = \"Gu\", role = c(\"aut\", \"cre\"), email = \"jiaqigu@usf.edu\"))", "Description": "A formula-driven framework for maximizing target functions\nvia the minorization-maximization (MM) algorithm. The package\nrepresents the target as a symbolic expression tree, infers its\ncurvature via disciplined-convex-programming rules, and\nconstructs a separable surrogate at each iterate using only\nJensen's inequality and the supporting hyperplane. The driver\nmaximizes the surrogate via block-coordinate Newton with line\nsearch, falling back to a multivariate step on any\nnon-separable residue. A formula interface accepts standard R\nexpressions (including `sum()` reductions and `X %*% theta`\ndesign-matrix products) so statistical models such as Poisson\nregression can be written in one line.", "License": "GPL-3", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "Config/testthat/edition": "3", "Repository": "https://gujq5.r-universe.dev", "Date/Publication": "2026-05-26 15:13:24 UTC", "RemoteUrl": "https://github.com/gujq5/mmad", "RemoteRef": "HEAD", "RemoteSha": "7a57a1be1dc89f35eaf6448417f35b93d2206ed6", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-26 18:25:25 UTC", "User": "root" }, "Author": "Xifen Huang [aut],\nJinfeng Xu [aut],\nJiaqi Gu [aut, cre]", "Maintainer": "Jiaqi Gu ", "MD5sum": "23704da6bfa695bfe86f9177adf67db0", "_user": "gujq5", "_type": "src", "_file": "MMAD_3.0.0.tar.gz", "_fileid": "b7473fa8b03cf50578c720538f6722998bdf19fb76bb0494640eb377b2020c0d", "_filesize": 159084, "_sha256": 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"mmad_call", "mmad_const", "mmad_var", "register_atom", "sign_of", "simplify_expr" ], "_help": [ { "page": "as_mmad_expr", "title": "Convert a formula or expression into an 'mmad_expr'", "topics": [ "as_mmad_expr" ] }, { "page": "curvature", "title": "Inferred curvature of an 'mmad_expr'", "topics": [ "curvature" ] }, { "page": "evaluate_expr", "title": "Evaluate an 'mmad_expr' at a parameter vector", "topics": [ "evaluate_expr" ] }, { "page": "Function_check", "title": "Diagnose an 'mmad_expr' (or formula) before optimization", "topics": [ "Function_check" ] }, { "page": "is_dcp", "title": "Whether an expression's curvature is provably one of affine/convex/concave", "topics": [ "is_dcp" ] }, { "page": "Math.mmad_expr", "title": "Mathematical functions for 'mmad_expr'", "topics": [ "Math.mmad_expr" ] }, { "page": "minorize_at", "title": "Build a surrogate of an 'mmad_expr' at a current iterate", "topics": [ "minorize_at" ] }, { "page": "mmad", "title": "Minorization-Maximization driver 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