Package: MSIMST 1.1
Qingyang Liu
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:
MSIMST_1.1.tar.gz
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://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/rh8liuqy/msimst/issues
Last updated 2 months agofrom:74634f06d5. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 16 2024 |
| R-4.5-linux-x86_64 | OK | Nov 16 2024 |
Exports:Gibbs_SamplerphiX_creg_simulation1reg_simulation2reg_simulation3WFPBB
Dependencies:dotCall64fieldsmapsMASSmvtnormrbibutilsRcppRcppArmadilloRdpackspamtruncnormviridisLite
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| The Associated Gibbs Sampler | Gibbs_Sampler |
| The 'MSIMST' package. | MSIMST-package MSIMST |
| The Function to Calculate the phiX Matrix for Estimating Single-Index Function | phiX_c |
| The Function for the Simulation Study without the Variable Selection | reg_simulation1 |
| The Function for the Simulation Study with the Variable Selection | reg_simulation2 |
| The Function for the Simulation Study with the Variable Selection and Survey Weights | reg_simulation3 |
| Weighted Finite Population Bayesian Bootstrap | WFPBB |
