Package: PEPBVS 2.1
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 distributions 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 over all models or the uniform on model dimension (a special case of the beta-binomial prior). The selection is performed by either implementing a full enumeration and evaluation 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 hypothesis testing, estimation and predictions under Bayesian model averaging, as well as, plotting and printing the results are also provided. The results can be compared to the ones obtained under other well-known priors on model parameters and model spaces.
Authors:
PEPBVS_2.1.tar.gz
PEPBVS_2.1.zip(r-4.5)PEPBVS_2.1.zip(r-4.4)PEPBVS_2.1.zip(r-4.3)
PEPBVS_2.1.tgz(r-4.4-x86_64)PEPBVS_2.1.tgz(r-4.4-arm64)PEPBVS_2.1.tgz(r-4.3-x86_64)PEPBVS_2.1.tgz(r-4.3-arm64)
PEPBVS_2.1.tar.gz(r-4.5-noble)PEPBVS_2.1.tar.gz(r-4.4-noble)
PEPBVS_2.1.tgz(r-4.4-emscripten)PEPBVS_2.1.tgz(r-4.3-emscripten)
PEPBVS.pdf |PEPBVS.html✨
PEPBVS/json (API)
NEWS
# Install 'PEPBVS' in R: |
install.packages('PEPBVS', repos = c('https://kcharmpi.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 11 days agofrom:2c6e258bf5. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 13 2024 |
R-4.5-win-x86_64 | OK | Nov 13 2024 |
R-4.5-linux-x86_64 | OK | Nov 13 2024 |
R-4.4-win-x86_64 | OK | Nov 13 2024 |
R-4.4-mac-x86_64 | OK | Nov 13 2024 |
R-4.4-mac-aarch64 | OK | Nov 13 2024 |
R-4.3-win-x86_64 | OK | Nov 13 2024 |
R-4.3-mac-x86_64 | OK | Nov 13 2024 |
R-4.3-mac-aarch64 | OK | Nov 13 2024 |
Exports:comparepriors.lmestimation.pepfull_enumeration_pepmc3_peppep.lmpeptestposteriorpredictive.pep
Dependencies:BASBayesVarSelbriocallrclicrayondescdiffobjdigestellipseevaluatefftwtoolsfsgluejsonlitelatticelifecyclemagrittrMASSMatrixmcmcsemvtnormpkgbuildpkgloadpraiseprocessxpsR6RcppRcppArmadilloRcppGSLrlangrprojroottestthatwaldowithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Bayesian variable selection using power-expected-posterior prior | PEPBVS-package |
Selected models under different choices of prior on the model parameters and the model space | comparepriors.lm |
Model averaged estimates | estimation.pep |
Heatmap for top models | image.pep |
Bayesian variable selection for Gaussian linear models using PEP through exhaustive search or with the MC3 algorithm | pep.lm |
Bayes factor for model comparison | peptest |
Plots for object of class pep | plot.pep |
Posterior predictive distribution under Bayesian model averaging | posteriorpredictive.pep |
(Point) Prediction under PEP approach | predict.pep |
Printing object of class pep | print.pep |
US Crime Data | UScrime_data |