The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.
Version: | 1.6.3 |
Depends: | R (≥ 3.0.2), methods, survival, splines |
Imports: | graphics, Rcpp (≥ 0.10.2), stats, mgcv, bbmle (≥ 1.0.20), fastGHQuad, deSolve, utils, parallel, mvtnorm |
LinkingTo: | Rcpp, RcppArmadillo, BH |
Suggests: | eha, testthat, ggplot2, lattice, readstata13, mstate, scales, survPen, flexsurv, timereg |
Published: | 2023-12-05 |
DOI: | 10.32614/CRAN.package.rstpm2 |
Author: | Mark Clements [aut, cre], Xing-Rong Liu [aut], Benjamin Christoffersen [aut], Paul Lambert [ctb], Lasse Hjort Jakobsen [ctb], Alessandro Gasparini [ctb], Gordon Smyth [cph], Patrick Alken [cph], Simon Wood [cph], Rhys Ulerich [cph] |
Maintainer: | Mark Clements <mark.clements at ki.se> |
BugReports: | https://github.com/mclements/rstpm2/issues |
License: | GPL-2 | GPL-3 |
URL: | https://github.com/mclements/rstpm2 |
NeedsCompilation: | yes |
Citation: | rstpm2 citation info |
Materials: | README NEWS |
In views: | Survival |
CRAN checks: | rstpm2 results |
Reference manual: | rstpm2.pdf |
Vignettes: |
Introduction to the rstpm2 Package \texttt{rstpm2}: a simple guide Predictions for Markov multi-state models Introduction to the predictnl function |
Package source: | rstpm2_1.6.3.tar.gz |
Windows binaries: | r-devel: rstpm2_1.6.3.zip, r-release: rstpm2_1.6.3.zip, r-oldrel: rstpm2_1.6.3.zip |
macOS binaries: | r-release (arm64): rstpm2_1.6.3.tgz, r-oldrel (arm64): rstpm2_1.6.3.tgz, r-release (x86_64): rstpm2_1.6.3.tgz, r-oldrel (x86_64): rstpm2_1.6.3.tgz |
Old sources: | rstpm2 archive |
Reverse depends: | cuRe |
Reverse imports: | afthd, eventPred, EventPredInCure, flexsurv, JointFPM, TukeyGH77 |
Reverse suggests: | biostat3, mexhaz, multinma, rsimsum, simsurv |
Please use the canonical form https://CRAN.R-project.org/package=rstpm2 to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.