c(person("John", "Wilshire", role = c("aut", "cre"), email = "john.wilshire@yale.edu"),
person("Richard", "Li", role = "aut"),
person("Ajay", "Ranipeta", role = "aut"))
John Wilshire [aut, cre],
Richard Li [aut],
Ajay Ranipeta [aut]
A wrapper for the 'Spatiotemporal Observation Annotation Tool' ('STOAT', <https://www.mol.org/stoat>) which allows users to run annotation jobs and retrieve results in the R environment.

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"))
graphics, Rcpp (>= 0.10.2), numDeriv, stats, mgcv, bbmle (>=
1.0.3)
Mark Clements [aut, cre], Xing-Rong Liu [aut], Paul Lambert [ctb]
R implementation of Stata's stpm2 function (flexible link-based survival models), with extensions to different smoothers and penalised models.

graphics, Rcpp (>= 0.10.2), numDeriv, stats, mgcv, bbmle (>=
1.0.3), fastGHQuad
R implementation of generalized survival models, where g(S(t(x))) for a link function g, survival S at time t with covariates x is modelled using a linear predictor. The main assumption is that the time effect(s) are smooth. For fully parametric models, 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. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models have all been extended to include left truncation, right censoring, interval censoring, frailties and normal random effects.

R implementation of generalized survival models, where 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, 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.