https://github.com/cran/rstpm2
Revision c12a9847539968aa375d4df8349a3a524e7c1bb5 authored by Mark Clements on 17 January 2019, 14:50:04 UTC, committed by cran-robot on 17 January 2019, 14:50:04 UTC
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Tip revision: c12a9847539968aa375d4df8349a3a524e7c1bb5 authored by Mark Clements on 17 January 2019, 14:50:04 UTC
version 1.4.5
Tip revision: c12a984
DESCRIPTION
Package: rstpm2
Type: Package
Title: Generalized Survival Models
Authors@R: c(person("Mark", "Clements", role = c("aut", "cre"),
		     email = "mark.clements@ki.se"),
	      person("Xing-Rong", "Liu", role = "aut",
		     email = "xingrong.liu@ki.se"),
	      person("Paul", "Lambert", role = "ctb", email="pl4@leicester.ac.uk"),
	      person("Lasse", "Hjort Jakobsen", role = "ctb", email="lasse.j@rn.dk"),
	      person("Alessandro", "Gasparini", role = "ctb"),
	      person("Gordon","Smyth", role="cph"),
	      person("Patrick","Alken", role="cph"),
	      person("Simon","Wood", role="cph"),
	      person("Rhys","Ulerich", role="cph"))
Version: 1.4.5
Date: 2019-01-13
Depends: R (>= 3.0.2), methods, survival, splines
Imports: graphics, Rcpp (>= 0.10.2), stats, mgcv, bbmle (>= 1.0.20),
        fastGHQuad
Suggests: eha, testthat
LinkingTo: Rcpp,RcppArmadillo
Maintainer: Mark Clements <mark.clements@ki.se>
Description: R implementation of generalized survival models (GSMs) and smooth accelerated failure time (AFT) models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x).
URL: http://github.com/mclements/rstpm2
BugReports: http://github.com/mclements/rstpm2/issues
License: GPL-2 | GPL-3
LazyData: yes
NeedsCompilation: yes
Packaged: 2019-01-17 15:03:16 UTC; marcle
Author: Mark Clements [aut, cre],
  Xing-Rong Liu [aut],
  Paul Lambert [ctb],
  Lasse Hjort Jakobsen [ctb],
  Alessandro Gasparini [ctb],
  Gordon Smyth [cph],
  Patrick Alken [cph],
  Simon Wood [cph],
  Rhys Ulerich [cph]
Repository: CRAN
Date/Publication: 2019-01-17 15:50:04 UTC
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