Incorporates a Bayesian monotonic single-index mixed-effect model with a multivariate skew-t likelihood, specifically designed to handle survey weights adjustments. Features include a simulation program and an associated Gibbs sampler for model estimation. The single-index function is constrained to be monotonic increasing, utilizing a customized Gaussian process prior for precise estimation. The model assumes random effects follow a canonical skew-t distribution, while residuals are represented by a multivariate Student-t distribution. Offers robust Bayesian adjustments to integrate survey weight information effectively.
The goal of MSIMST is to provide a Bayesian monotonic single-index mixed-effect model incorporating a multivariate skew-t likelihood with survey weights adjustments. This package includes a simulation program and the associated Gibbs sampler. The single-index function is modeled as a monotonic increasing function, with a tailored Gaussian process prior to ensure accurate estimation. Random effects are assumed to follow the canonical skew-t distribution, while residuals are modeled using the multivariate Student-t distribution. Additionally, the package provides Bayesian adjustment for survey weight information.
You can install the development version of MSIMST like so:
devtools::install_github(repo = "https://github.com/rh8liuqy/MSIMST")
Users can access the vignette:
library(MSIMST)
vignette("MSIMST_vignette",package = "MSIMST")