Simulates regression models,
    including both simple regression and generalized linear mixed
    models with up to three level of nesting. Power simulations that are
    flexible allowing the specification of missing data, unbalanced designs,
    and different random error distributions are built into the package.

Routine that allows the user to run several goodness-of-fit tests.  
    It also combines the tests and returns a properly adjusted family-wise p value.
    Details can be found in <arXiv:2007.04727>.

Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.

Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies, following a general simulation workflow that closely aligns with the approach described by Morris, White, and Crowther (2019) <DOI:10.1002/sim.8086>. Also includes functions for calculating bootstrap confidence intervals (including normal, basic, studentized, percentile, bias-corrected, and bias-corrected-and-accelerated) with tidy output, as well as for extrapolating confidence interval coverage rates and hypothesis test rejection rates following techniques suggested by Boos and Zhang (2000) <DOI:10.1080/01621459.2000.10474226>.

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.