Provides tools for simulating spatially dependent predictors (continuous or binary),
    which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous
    predictors are generated using traditional multivariate normal distributions or Gauss Markov random
    fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288>
    and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using
    a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors 
	exhibiting spatial clustering can also be easily specified by the user.

Monitoring reporting rates of subject-level clinical events (e.g.
  adverse events, protocol deviations) reported by clinical trial sites is an
  important aspect of risk-based quality monitoring strategy. Sites that are 
  under-reporting or over-reporting events can be detected using bootstrap
  simulations during which patients are redistributed between sites. Site-specific
  distributions of event reporting rates  are generated that are used to assign
  probabilities to the observed reporting rates.
  (Koneswarakantha 2024 <doi:10.1007/s43441-024-00631-8>).

Allows user to conduct a simulation based quantitative
             bias analysis using covariate structures generated with individual-level
             data to characterize the bias arising from unmeasured confounding. Users
             can specify their desired data generating mechanisms to simulate data
             and quantitatively summarize findings in an end-to-end application
             using this package.

Functions for obtaining p-values (for hypothesis tests), confidence intervals, and multivariate confidence sets. In particular, the method is compatible with differentially private dataset, as long as the privacy mechanism is known. For more details, see Awan and Wang (2024), "Simulation-based, Finite-sample Inference for Privatized Data", <doi:10.48550/arXiv.2303.05328>.