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 of Adverse Event (AE) reporting in clinical trials is 
  important for patient safety. Sites that are under-reporting AEs can be detected 
  using Bootstrap-based simulations that simulate overall AE reporting. Based on the 
  simulation an AE under-reporting probability is assigned to each site in a 
  given trial (Koneswarakantha 2021 <doi:10.1007/s40264-020-01011-5>).

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>.

Provides some code to run simulations of state-space models, and then
             use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) 
             algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a bootstrap particle
             filter based particle Markov chain Monte Carlo (PMCMC) algorithm 
             (Andrieu et al., 2010 <doi:10.1111/j.1467-9868.2009.00736.x>). 
             Also provides functions to plot and summarise the outputs.

Provides a suite of helper functions to support Bayesian Kernel 
    Machine Regression (BKMR) analyses in environmental health research. It 
    enables the simulation of realistic multivariate exposure data using 
    Multivariate Skewed Gamma distributions, estimation of distributional 
    parameters by subgroup, and application of adaptive, data-driven thresholds 
    for feature selection via Posterior Inclusion Probabilities (PIPs). It is 
    especially suited for handling skewed exposure data and enhancing the 
    interpretability of BKMR results through principled variable selection. The
    methodology is shown in Hasan et. al. (2025) <doi:10.1101/2025.04.14.25325822>.