Based on the illness-death model a large number of clinical
    trials with oncology endpoints progression-free survival (PFS) and
    overall survival (OS) can be simulated, see Meller, Beyersmann and
    Rufibach (2019) <doi:10.1002/sim.8295>.  The simulation set-up allows
    for random and event-driven censoring, an arbitrary number of
    treatment arms, staggered study entry and drop-out.  Exponentially,
    Weibull and piecewise exponentially distributed survival times can be
    generated. The correlation between PFS and OS can be calculated.

Functions to run and assist four
  different similarity measures. The similarity
  measures included are: longest common
  subsequence (LCSS), Frechet distance, edit distance
  and dynamic time warping (DTW). Each of these
  similarity measures can be calculated from two
  n-dimensional trajectories, both in matrix form.

Allows a Simile model saved as a compiled binary to be
  loaded, parameterized, executed and interrogated. This version works 
  with Simile v5.97 on.

Provides an efficient and very flexible framework to
    conduct data-driven epidemiological modeling in realistic large
    scale disease spread simulations. The framework integrates
    infection dynamics in subpopulations as continuous-time Markov
    chains using the Gillespie stochastic simulation algorithm and
    incorporates available data such as births, deaths and movements
    as scheduled events at predefined time-points. Using C code for
    the numerical solvers and 'OpenMP' (if available) to divide work
    over multiple processors ensures high performance when simulating
    a sample outcome. One of our design goals was to make the package
    extendable and enable usage of the numerical solvers from other R
    extension packages in order to facilitate complex epidemiological
    research. The package contains template models and can be extended
    with user-defined models. For more details see the paper by
    Widgren, Bauer, Eriksson and Engblom (2019)
    <doi:10.18637/jss.v091.i12>. The package also provides
    functionality to fit models to time series data using the
    Approximate Bayesian Computation Sequential Monte Carlo
    ('ABC-SMC') algorithm of Toni and others (2009)
    <doi:10.1098/rsif.2008.0172>.