  Over the last five decades, we have seen strong methodological advances in
survival analysis, mainly in two separate strands: One strand is based on a
parametric approach that assumes some response distribution. More prominent,
however, is the strand of flexible methods which rely mainly on
non-/semi-parametric estimation. As the methodological landscape continues to
evolve, the task of navigating through the multitude of methods and identifying
corresponding available software resources is becoming increasingly difficult.
This task becomes particularly challenging in more complex scenarios, such as
when dealing with interval-censored or clustered survival data,
non-proportionality, or dependent censoring.
  In this tutorial, we explore the potential of using smooth transformation
models for survival analysis in the R system for statistical computing. These
models provide a unified maximum likelihood framework that covers a range of
survival models, including well-established ones such as the Weibull model and
a fully parameterised version of the famous Cox proportional hazards model, as
well as extensions to more complex scenarios. We explore smooth transformation
models for non-proportional/crossing hazards, dependent censoring, clustered
observations and extensions towards personalised medicine within this
framework.
  By fitting these models to survival data from a two-arm randomised controlled
trial on rectal cancer therapy, we demonstrate how survival analysis tasks can
be seamlessly navigated within the smooth transformation model framework in R.
This is achieved by the implementation provided by the "tram" package and few
related packages.
