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The first penalty enforces variable selection\nthrough a LASSO penalty, while the second preserves gene-gene\ncorrelations by incorporating Laplacian-based constraints,\nensuring that biologically relevant network structures are\nmaintained. Using censored survival data, the method enables\nthe identification of predictive biomarkers and pathways with\npotential relevance for target therapies. Model estimation is\nperformed via proximal optimization algorithms combined with\ncross-validation for reliable tuning. To enhance\ninterpretability, dedicated utility functions are implemented\nto consolidate results, yielding biologically coherent insights\nthat can support personalized medicine and contribute to\nimproved patient outcomes.", "VignetteBuilder": "knitr", "License": "GPL (>= 3)", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "LazyData": "true", "LazyDataCompression": "bzip2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-09 11:08:28 UTC", "User": "root" }, "Author": "Maura Mecchi [aut, cre], Antonella Iuliano [aut]", "Config/pak/sysreqs": "zlib1g-dev", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-06-09 08:43:52 UTC", "RemoteUrl": "https://github.com/cran/NetSurvProx", "RemoteRef": "HEAD", "RemoteSha": "210154a09bc18f64e8f6fed382a097a00743e889", "MD5sum": "924f14bef621c249e064f57a75419c3d", "_user": "cran", "_type": "src", "_file": "NetSurvProx_1.0.0.tar.gz", "_fileid": "fead90ad1cef83590a8a9f3ae87f8586170654d1fc588a6719e784b6bc705a71", "_filesize": 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