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  "Title": "Smooth Survival Models, Including Generalized Survival Models",
  "Authors@R": "c(person(\"Mark\", \"Clements\", role = c(\"aut\", \"cre\"),\nemail = \"mark.clements@ki.se\"),\nperson(\"Xing-Rong\", \"Liu\", role = \"aut\",\nemail = \"xingrong.liu@ki.se\"),\nperson(\"Benjamin\", \"Christoffersen\", role = \"aut\",\nemail = \"benjamin.christoffersen@ki.se\"),\nperson(\"Paul\", \"Lambert\", role = \"ctb\", email=\"pl4@leicester.ac.uk\"),\nperson(\"Lasse\", \"Hjort Jakobsen\", role = \"ctb\", email=\"lasse.j@rn.dk\"),\nperson(\"Alessandro\", \"Gasparini\", role = \"ctb\"),\nperson(\"Gordon\",\"Smyth\", role=\"cph\"),\nperson(\"Patrick\",\"Alken\", role=\"cph\"),\nperson(\"Simon\",\"Wood\", role=\"cph\"),\nperson(\"Rhys\",\"Ulerich\", role=\"cph\"))",
  "Version": "1.7.0",
  "Maintainer": "Mark Clements <mark.clements@ki.se>",
  "Description": "R implementation of generalized survival models (GSMs),\nsmooth accelerated failure time (AFT) models and Markov\nmulti-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link\nfunction g, survival S at time t with covariates x and a linear\npredictor eta(t,x). The main assumption is that the time\neffect(s) are smooth <doi:10.1177/0962280216664760>. For fully\nparametric models with natural splines, this re-implements\nStata's 'stpm2' function, which are flexible parametric\nsurvival models developed by Royston and colleagues. We have\nextended the parametric models to include any smooth parametric\nsmoothers for time. We have also extended the model to include\nany smooth penalized smoothers from the 'mgcv' package, using\npenalized likelihood. These models include left truncation,\nright censoring, interval censoring, gamma frailties and normal\nrandom effects <doi:10.1002/sim.7451>, and copulas. For the\nsmooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline\nsurvival function S_0(t)=exp(-exp(eta_0(t))) is modelled for\nnatural splines for eta_0, and the time-dependent cumulative\nacceleration factor eta(t,x)=\\int_0^t exp(eta_1(u,x)) du for\nlog acceleration factor eta_1(u,x). The Markov multi-state\nmodels allow for a range of models with smooth transitions to\npredict transition probabilities, length of stay, utilities and\ncosts, with differences, ratios and standardisation.",
  "URL": "https://github.com/mclements/rstpm2",
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  "Date/Publication": "2025-08-27 17:29:09 UTC",
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  "Author": "Mark Clements [aut, cre],\nXing-Rong Liu [aut],\nBenjamin Christoffersen [aut],\nPaul Lambert [ctb],\nLasse Hjort Jakobsen [ctb],\nAlessandro Gasparini [ctb],\nGordon Smyth [cph],\nPatrick Alken [cph],\nSimon Wood [cph],\nRhys Ulerich [cph]",
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    "anova",
    "bhazard",
    "BIC",
    "coef",
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    "eform",
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    "hrModel",
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    "lines",
    "markov_msm",
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    "plot",
    "predict",
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    "pstpm2",
    "qAICc",
    "ratio_markov_msm",
    "residuals",
    "simulate",
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    "stpm2",
    "summary",
    "update",
    "vcov",
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    "vuniroot",
    "zeroModel"
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      "title": "German breast cancer data from Stata.",
      "object": "brcancer",
      "class": [
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        "hormon",
        "x1",
        "x2",
        "x3",
        "x4",
        "x5",
        "x6",
        "x7",
        "rectime",
        "censrec",
        "x4a",
        "x4b",
        "x5e"
      ],
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      "table": true,
      "tojson": true
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    {
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      "title": "Colon cancer.",
      "object": "colon",
      "class": [
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      ],
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        "age",
        "stage",
        "mmdx",
        "yydx",
        "surv_mm",
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        "status",
        "subsite",
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        "dx",
        "exit"
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      "table": true,
      "tojson": true
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      "class": [
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        "w"
      ],
      "rows": 200,
      "table": true,
      "tojson": true
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      "name": "popmort",
      "title": "Background mortality rates for the colon dataset.",
      "object": "popmort",
      "class": [
        "data.frame"
      ],
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        "prob",
        "rate",
        "age",
        "year"
      ],
      "rows": 10600,
      "table": true,
      "tojson": true
    }
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    {
      "page": "aft",
      "title": "Parametric accelerated failure time model with smooth time functions",
      "topics": [
        "aft"
      ]
    },
    {
      "page": "aft-class",
      "title": "Class \"stpm2\" ~~~",
      "topics": [
        "aft-class",
        "lines,aft-method",
        "plot,aft,missing-method",
        "predict,aft-method"
      ]
    },
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      "title": "Placemarker function for a baseline hazard function.",
      "topics": [
        "bhazard"
      ]
    },
    {
      "page": "brcancer",
      "title": "German breast cancer data from Stata.",
      "topics": [
        "brcancer"
      ]
    },
    {
      "page": "coef",
      "title": "Generic method to update the coef in an object.",
      "topics": [
        "coef<-"
      ]
    },
    {
      "page": "colon",
      "title": "Colon cancer.",
      "topics": [
        "colon"
      ]
    },
    {
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      "title": "Test for a time-varying effect in the 'coxph' model",
      "topics": [
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      ]
    },
    {
      "page": "eform",
      "title": "S3 method for to provide exponentiated coefficents with confidence intervals.",
      "topics": [
        "eform",
        "eform.default",
        "eform.stpm2"
      ]
    },
    {
      "page": "grad",
      "title": "gradient function (internal function)",
      "topics": [
        "grad"
      ]
    },
    {
      "page": "gsm",
      "title": "Parametric and penalised generalised survival models",
      "topics": [
        "gsm",
        "pstpm2",
        "stpm2"
      ]
    },
    {
      "page": "gsm_design",
      "title": "Extract design information from an stpm2/gsm object and newdata for use in C++",
      "topics": [
        "gsm_design"
      ]
    },
    {
      "page": "gsm.control",
      "title": "Defaults for the gsm call",
      "topics": [
        "gsm.control"
      ]
    },
    {
      "page": "incrVar",
      "title": "Utility that returns a function to increment a variable in a data-frame.",
      "topics": [
        "incrVar"
      ]
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    {
      "page": "legendre.quadrature.rule.200",
      "title": "Legendre quadrature rule for n=200.",
      "topics": [
        "legendre.quadrature.rule.200"
      ]
    },
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      "page": "lines",
      "title": "S3 methods for lines",
      "topics": [
        "lines.pstpm2",
        "lines.stpm2"
      ]
    },
    {
      "page": "markov_msm",
      "title": "Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models.",
      "topics": [
        "addModel",
        "aftModel",
        "as.data.frame.markov_msm",
        "as.data.frame.markov_msm_diff",
        "as.data.frame.markov_msm_ratio",
        "collapse_markov_msm",
        "diff",
        "diff.markov_msm",
        "hazFun",
        "hrModel",
        "markov_msm",
        "plot.markov_msm",
        "ratio_markov_msm",
        "rbind.markov_msm",
        "splineFun",
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        "standardise.markov_msm",
        "subset.markov_msm",
        "transform.markov_msm",
        "vcov.markov_msm",
        "zeroModel"
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    {
      "page": "markov_sde",
      "title": "Predictions for continuous time, nonhomogeneous Markov multi-state models using Aalen's additive hazards models.",
      "topics": [
        "as.data.frame.markov_sde",
        "markov_sde",
        "plot.markov_sde",
        "standardise.markov_sde"
      ]
    },
    {
      "page": "nsx",
      "title": "Generate a Basis Matrix for Natural Cubic Splines (with eXtensions)",
      "topics": [
        "nsx"
      ]
    },
    {
      "page": "nsxD",
      "title": "Generate a Basis Matrix for the first derivative of Natural Cubic Splines (with eXtensions)",
      "topics": [
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      "page": "numDeltaMethod",
      "title": "Calculate numerical delta method for non-linear predictions.",
      "topics": [
        "numDeltaMethod"
      ]
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    {
      "page": "plot-methods",
      "title": "plots for an stpm2 fit",
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        "plot,pstpm2-method",
        "plot,stpm2-method",
        "plot-methods"
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      "page": "popmort",
      "title": "Background mortality rates for the colon dataset.",
      "topics": [
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      "title": "Predicted values for an stpm2 or pstpm2 fit",
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        "predict,stpm2-method",
        "predict-methods"
      ]
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      "page": "predict.nsx",
      "title": "Evaluate a Spline Basis",
      "topics": [
        "predict.nsx"
      ]
    },
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      "page": "predictnl",
      "title": "Estimation of standard errors using the numerical delta method.",
      "topics": [
        "confint.predictnl",
        "predict.formula",
        "predictnl",
        "predictnl.default",
        "predictnl.lm"
      ]
    },
    {
      "page": "predictnl-methods",
      "title": "Estimation of standard errors using the numerical delta method.",
      "topics": [
        "predictnl,aft-method",
        "predictnl,mle2-method",
        "predictnl,pstpm2-method",
        "predictnl,stpm2-method",
        "predictnl-methods"
      ]
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      "page": "pstpm2-class",
      "title": "Class \"pstpm2\"",
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