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MSIMST: Bayesian Monotonic Single-Index Regression Model with the Skew-TLikelihood

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.

Version:	1.1
Depends:	R (≥ 3.4.0)
Imports:	MASS (≥ 7.3-58.4),Rcpp (≥ 1.0.12),mvtnorm (≥ 1.2-4),fields (≥ 15.2), parallel (≥ 4.3.0),truncnorm (≥ 1.0-9),Rdpack (≥ 2.6)
LinkingTo:	Rcpp,RcppArmadillo
Suggests:	lattice (≥ 0.21-8),HDInterval (≥ 0.2.4),latex2exp (≥0.9.6),posterior (≥ 1.5.0)
Published:	2024-09-16
DOI:	10.32614/CRAN.package.MSIMST
Author:	Qingyang Liu [aut, cre], Debdeep Pati [aut], Dipankar Bandyopadhyay [aut]
Maintainer:	Qingyang Liu <rh8liuqy at gmail.com>
License:	GPL (≥ 3)
URL:	https://github.com/rh8liuqy/MSIMST
NeedsCompilation:	yes
Materials:	README,NEWS
CRAN checks:	MSIMST results

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