Package: MSIMST 1.1

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.

Authors:Qingyang Liu [aut, cre], Debdeep Pati [aut], Dipankar Bandyopadhyay [aut]

MSIMST_1.1.tar.gz
MSIMST_1.1.zip(r-4.5)MSIMST_1.1.zip(r-4.4)MSIMST_1.1.zip(r-4.3)
MSIMST_1.1.tgz(r-4.4-x86_64)MSIMST_1.1.tgz(r-4.4-arm64)MSIMST_1.1.tgz(r-4.3-x86_64)MSIMST_1.1.tgz(r-4.3-arm64)
MSIMST_1.1.tar.gz(r-4.5-noble)MSIMST_1.1.tar.gz(r-4.4-noble)
MSIMST_1.1.tgz(r-4.4-emscripten)MSIMST_1.1.tgz(r-4.3-emscripten)
MSIMST.pdf |MSIMST.html
MSIMST/json (API)
NEWS

# Install 'MSIMST' in R:
install.packages('MSIMST', repos = c('https://rh8liuqy.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/rh8liuqy/msimst/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

6 exports 1 stars 4.00 score 12 dependencies

Last updated 25 days agofrom:a1fe05ff50. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 17 2024
R-4.5-win-x86_64OKSep 17 2024
R-4.5-linux-x86_64OKSep 17 2024
R-4.4-win-x86_64OKSep 17 2024
R-4.4-mac-x86_64OKSep 17 2024
R-4.4-mac-aarch64OKSep 17 2024
R-4.3-win-x86_64OKSep 17 2024
R-4.3-mac-x86_64OKSep 17 2024
R-4.3-mac-aarch64OKSep 17 2024

Exports:Gibbs_SamplerphiX_creg_simulation1reg_simulation2reg_simulation3WFPBB

Dependencies:dotCall64fieldsmapsMASSmvtnormrbibutilsRcppRcppArmadilloRdpackspamtruncnormviridisLite

Robust Statistical Modeling for Quantifying Periodontal Disease: A Single Index Mixed-Effects Approach with Skewed Random Effects and Heavy-Tailed Residuals

Rendered fromMSIMST_vignette.Rnwusingutils::Sweaveon Sep 17 2024.

Last update: 2024-09-16
Started: 2024-08-07