{ "_id": "6a12a906acfb0bcc41d15878", "Package": "survPen", "Title": "Multidimensional Penalized Splines for (Excess) Hazard Models,\nRelative Mortality Ratio Models and Marginal Intensity Models", "Version": "2.0.3", "Authors@R": "c(person(given = \"Mathieu\",\nfamily = \"Fauvernier\",\nrole = c(\"aut\", \"cre\"),\nemail = \"mathieu.fauvernier@gmail.com\"),\nperson(given = \"Laurent\",\nfamily = \"Roche\",\nrole = \"aut\"),\nperson(given = \"Laurent\",\nfamily = \"Remontet\",\nrole = \"aut\"),\nperson(given = \"Zoe\",\nfamily = \"Uhry\",\nrole = \"ctb\"),\nperson(given = \"Nadine\",\nfamily = \"Bossard\",\nrole = \"ctb\"),\nperson(given = \"Elsa\",\nfamily = \"Coz\",\nrole = \"ctb\"))", "Description": "Fits (excess) hazard, relative mortality ratio or marginal\nintensity models with multidimensional penalized splines\nallowing for time-dependent effects, non-linear effects and\ninteractions between several continuous covariates. In survival\nand net survival analysis, in addition to modelling the effect\nof time (via the baseline hazard), one has often to deal with\nseveral continuous covariates and model their functional forms,\ntheir time-dependent effects, and their interactions. Model\nspecification becomes therefore a complex problem and penalized\nregression splines represent an appealing solution to that\nproblem as splines offer the required flexibility while\npenalization limits overfitting issues. Current implementations\nof penalized survival models can be slow or unstable and\nsometimes lack some key features like taking into account\nexpected mortality to provide net survival and excess hazard\nestimates. In contrast, survPen provides an automated, fast,\nand stable implementation (thanks to explicit calculation of\nthe derivatives of the likelihood) and offers a unified\nframework for multidimensional penalized hazard and excess\nhazard models. Later versions (>2.0.0) include penalized models\nfor relative mortality ratio, and marginal intensity in\nrecurrent event setting. survPen may be of interest to those\nwho 1) analyse any kind of time-to-event data: mortality,\ndisease relapse, machinery breakdown, unemployment, etc 2) wish\nto describe the associated hazard and to understand which\npredictors impact its dynamics, 3) wish to model the relative\nmortality ratio between a cohort and a reference population, 4)\nwish to describe the marginal intensity for recurrent event\ndata. See Fauvernier et al. (2019a) \nfor an overview of the package and Fauvernier et al. (2019b)\n for the method.", "License": "GPL-3 | file LICENSE", "URL": "https://github.com/fauvernierma/survPen", "BugReports": "https://github.com/fauvernierma/survPen/issues", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "VignetteBuilder": "knitr", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-24 07:22:28 UTC", "User": "root" }, "Author": "Mathieu Fauvernier [aut, cre], Laurent Roche [aut], Laurent\nRemontet [aut], Zoe Uhry [ctb], Nadine Bossard [ctb], Elsa Coz\n[ctb]", "Maintainer": "Mathieu Fauvernier ", "Repository": "https://fauvernierma.r-universe.dev", "Date/Publication": "2026-03-25 10:29:01 UTC", "RemoteUrl": "https://github.com/fauvernierma/survpen", "RemoteRef": "HEAD", "RemoteSha": "b807ddfd2f926ceb61835f9065943bf76324a40c", "MD5sum": "325e3e8ba59683b100c78a22472a6117", "_user": "fauvernierma", "_type": "src", "_file": "survPen_2.0.3.tar.gz", "_fileid": "bd27a89bbc778cf818463e9b9bf89f4c7aef08b8f891b5afa9315e58d01ea142", "_filesize": 4708928, 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