Package: PEPBVS 1.0
Konstantina Charmpi
PEPBVS: Bayesian Variable Selection using Power-Expected-Posterior Prior
Performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior either the power-expected-posterior (PEP) or the intrinsic (a special case of the former) (Fouskakis and Ntzoufras (2022) <doi:10.1214/21-BA1288>, Fouskakis and Ntzoufras (2020) <doi:10.3390/econometrics8020017>). The prior distribution on model space is the uniform on model space or the uniform on model dimension (a special case of the beta-binomial prior). The selection can be done either with full enumeration of all possible models or using the Markov Chain Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) <doi:10.2307/1403615>). Complementary functions for making predictions, as well as plotting and printing the results are also provided.
Authors:
PEPBVS_1.0.tar.gz
PEPBVS_1.0.tar.gz(r-4.5-noble)PEPBVS_1.0.tar.gz(r-4.4-noble)
PEPBVS_1.0.tgz(r-4.4-emscripten)PEPBVS_1.0.tgz(r-4.3-emscripten)
PEPBVS.pdf |PEPBVS.html✨
PEPBVS/json (API)
| # Install 'PEPBVS' in R: |
| install.packages('PEPBVS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- UScrime_data - US Crime Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:e702ed9efb. Checks:OK: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 13 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 13 2024 |
Exports:full_enumeration_pepmc3_pep
Dependencies:latticeMatrixRcppRcppArmadilloRcppGSL
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bayesian variable selection using power-expected-posterior prior | PEPBVS-package |
| Bayesian variable selection through exhaustive search | full_enumeration_pep |
| Heatmap for top models | image.pep |
| Bayesian variable selection with MC3 algorithm | mc3_pep |
| Plots for object of class pep | plot.pep |
| Prediction under PEP approach | predict.pep |
| Printing object of class pep | print.pep |
| US Crime Data | UScrime_data |
