Package: r-cran-rstpm2
Version: 1.7.1-1
Architecture: arm64
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 3752
Depends: r-api-4.0, r-cran-survival, r-cran-rcpp (>= 0.10.2), r-cran-mgcv, r-cran-bbmle (>= 1.0.20), r-cran-fastghquad, r-cran-mvtnorm, r-cran-numderiv, r-cran-lsoda, r-cran-rcpparmadillo, libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14)
Recommends: r-cran-testthat, r-cran-ggplot2, r-cran-lattice, r-cran-readstata13, r-cran-mstate, r-cran-scales, r-cran-timereg
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=rstpm2
Description: Smooth Survival Models, Including Generalized Survival Models
 R implementation of generalized survival models (GSMs), smooth
 accelerated failure time (AFT) models and Markov multi-state 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 <doi:10.1177/0962280216664760>. 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 <doi:10.1002/sim.7451>,
 and copulas. 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).
 The Markov multi-state models allow for a range of models with smooth
 transitions to predict transition probabilities, length of stay, utilities
 and costs, with differences, ratios and standardisation.
