Simulates data from model objects (e.g., from lm(), glm()),
    and plots this along with the original data to compare how well the
    simulated data matches the original data to determine model fit.

A general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort.  The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results.  It follows a clear object-oriented design and supports parallel computing to increase computational performance.

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>.