Provides tools to simulate and analyze survival data with interval-, 
  left-, right-, and uncensored observations under common parametric distributions, 
  including "Weibull", "Exponential", "Log-Normal", "Log-Logistic", "Gamma", 
  "Gompertz", "Normal", "Logistic", and "EMV". The package supports both direct 
  maximum likelihood estimation and imputation-based methods, making it suitable 
  for methodological research, simulation benchmarking, and teaching. A web-based 
  companion app is also available for demonstration purposes.

Contains tests for association between a set of genetic variants and multiple correlated outcomes that are interval censored. Interval-censored data arises when the exact time of the onset of an outcome of interest is unknown but known to fall between two time points.

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 v6 on.

A fast implementation of the weighted information similarity aggregation (WISE) 
    test for detecting serial dependence, particularly suited for high-dimensional and 
    non-Euclidean time series. Includes functions for constructing similarity matrices 
    and conducting hypothesis testing. Users can use different similarity 
    measures and define their own weighting schemes. For more details see Q Zhu, M Liu, 
    Y Han, D Zhou (2025) <doi:10.48550/arXiv.2509.05678>.