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>.

Provides estimation of simultaneous bootstrap and asymptotic confidence intervals for diversity indices, namely the Shannon and the Simpson index. Several pre--specified multiple comparison types are available to choose. Further user--defined contrast matrices are applicable. In addition, simboot estimates adjusted as well as unadjusted p--values for two of the three proposed bootstrap methods. Further simboot allows for comparing biological diversities of two or more groups while simultaneously testing a user-defined selection of Hill numbers of orders q, which are considered as appropriate and useful indices for measuring diversity.