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MSIMST: Bayesian Monotonic Single-Index Regression Model with the Skew-T Likelihood
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.
Package details |
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| Author | Qingyang Liu [aut, cre] (<https://orcid.org/0000-0003-3265-6330>), Debdeep Pati [aut], Dipankar Bandyopadhyay [aut] |
| Maintainer | Qingyang Liu <rh8liuqy@gmail.com> |
| License | GPL (>= 3) |
| Version | 1.1 |
| URL | https://github.com/rh8liuqy/MSIMST |
| Package repository | View on CRAN |
| Installation |
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