Package: projpred 2.8.0
projpred: Projection Predictive Feature Selection
Performs projection predictive feature selection for generalized linear models (Piironen, Paasiniemi, and Vehtari, 2020, <doi:10.1214/20-EJS1711>) with or without multilevel or additive terms (Catalina, Bürkner, and Vehtari, 2022, <https://proceedings.mlr.press/v151/catalina22a.html>), for some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2023, <arxiv:2301.01660>), and for many other regression models (using the latent projection by Catalina, Bürkner, and Vehtari, 2021, <arxiv:2109.04702>, which can also be applied to most of the former models). The package is compatible with the 'rstanarm' and 'brms' packages, but other reference models can also be used. See the vignettes and the documentation for more information and examples.
Authors:
projpred_2.8.0.tar.gz
projpred_2.8.0.tar.gz(r-4.5-noble)projpred_2.8.0.tar.gz(r-4.4-noble)
projpred_2.8.0.tgz(r-4.4-emscripten)projpred_2.8.0.tgz(r-4.3-emscripten)
projpred.pdf |projpred.html✨
projpred/json (API)
NEWS
# Install 'projpred' in R: |
install.packages('projpred', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/stan-dev/projpred/issues44 issues
Pkgdown site:https://mc-stan.org
- df_binom - Binomial toy example
- df_gaussian - Gaussian toy example
- mesquite - Mesquite data set
Last updated 1 years agofrom:157774b9ad. Checks:3 OK. Indexed: no.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 09 2025 |
R-4.5-linux-x86_64 | OK | Mar 09 2025 |
R-4.4-linux-x86_64 | OK | Mar 09 2025 |
Exports:augdat_ilink_binomaugdat_link_binombreak_up_matrix_termcl_aggcv_foldscv_idscv_proportionscv_varselcvfoldsdo_callextend_familyforce_search_termsget_refmodelinit_refmodelperformancespredictor_termsproj_linpredproj_predictprojectrankingrun_cvfunsolution_termsStudent_tsuggest_sizevarsely_wobs_offs
Dependencies:abindbackportsbootcheckmateclicolorspacedata.tabledescdistributionalfansifarvergamm4genericsggplot2gluegtablegtoolsisobandjsonlitelabelinglatticelifecyclelme4loomagrittrMASSMatrixmatrixStatsmclogitmemiscmgcvminqamunsellmvtnormnlmenloptrnnetnumDerivordinalpillarpkgconfigposteriorR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackreformulasrlangrstantoolsscalestensorAtibbleucminfutf8vctrsviridisLitewithryaml
Citation
To cite the 'projpred' R package:
Piironen J, Paasiniemi M, Catalina A, Weber F, Vehtari A (2023). “projpred: Projection Predictive Feature Selection.” R package version 2.8.0, https://mc-stan.org/projpred/.
To cite the 'projpred' comparison paper:
Piironen J, Vehtari A (2017). “Comparison of Bayesian Predictive Methods for Model Selection.” Statistics and Computing, 27(3), 711–735. doi:10.1007/s11222-016-9649-y.
To cite the 'projpred' GLM paper:
Piironen J, Paasiniemi M, Vehtari A (2020). “Projective Inference in High-Dimensional Problems: Prediction and Feature Selection.” Electronic Journal of Statistics, 14(1), 2155–2197. doi:10.1214/20-EJS1711.
To cite the 'projpred' GLMMs, GAMs, and GAMMs paper:
Catalina A, Bürkner P, Vehtari A (2022). “Projection Predictive Inference for Generalized Linear and Additive Multilevel Models.” In Camps-Valls G, Ruiz F, Valera I (eds.), Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, volume 151 series Proceedings of Machine Learning Research, 4446–4461. https://proceedings.mlr.press/v151/catalina22a.html.
To cite the 'projpred' augmented-data projection paper:
Weber F, Glass Ä, Vehtari A (2023). “Projection Predictive Variable Selection for Discrete Response Families with Finite Support.” doi:10.48550/arXiv.2301.01660.
To cite the 'projpred' latent projection paper:
Catalina A, Bürkner P, Vehtari A (2021). “Latent Space Projection Predictive Inference.” doi:10.48550/arXiv.2109.04702.
Corresponding BibTeX entries:
@Misc{, title = {{{projpred}}: {{Projection}} Predictive Feature Selection}, author = {Juho Piironen and Markus Paasiniemi and Alejandro Catalina and Frank Weber and Aki Vehtari}, year = {2023}, note = {R package version 2.8.0}, url = {https://mc-stan.org/projpred/}, encoding = {UTF-8}, }
@Article{, title = {Comparison of {{Bayesian}} Predictive Methods for Model Selection}, author = {Juho Piironen and Aki Vehtari}, year = {2017}, journal = {Statistics and Computing}, volume = {27}, number = {3}, pages = {711--735}, doi = {10.1007/s11222-016-9649-y}, }
@Article{, title = {Projective Inference in High-Dimensional Problems: {{Prediction}} and Feature Selection}, author = {Juho Piironen and Markus Paasiniemi and Aki Vehtari}, year = {2020}, journal = {Electronic Journal of Statistics}, volume = {14}, number = {1}, pages = {2155--2197}, doi = {10.1214/20-EJS1711}, }
@InProceedings{, title = {Projection Predictive Inference for Generalized Linear and Additive Multilevel Models}, booktitle = {Proceedings of {{The}} 25th {{International Conference}} on {{Artificial Intelligence}} and {{Statistics}}}, author = {Alejandro Catalina and Paul-Christian Bürkner and Aki Vehtari}, editor = {Gustau Camps-Valls and Francisco J. R. Ruiz and Isabel Valera}, year = {2022}, month = {28--30 Mar}, series = {Proceedings of {{Machine Learning Research}}}, volume = {151}, pages = {4446--4461}, publisher = {{PMLR}}, url = {https://proceedings.mlr.press/v151/catalina22a.html}, encoding = {UTF-8}, }
@Misc{, title = {Projection Predictive Variable Selection for Discrete Response Families with Finite Support}, author = {Frank Weber and Änne Glass and Aki Vehtari}, year = {2023}, publisher = {{arXiv}}, doi = {10.48550/arXiv.2301.01660}, encoding = {UTF-8}, }
@Misc{, title = {Latent Space Projection Predictive Inference}, author = {Alejandro Catalina and Paul Bürkner and Aki Vehtari}, year = {2021}, publisher = {{arXiv}}, doi = {10.48550/arXiv.2109.04702}, encoding = {UTF-8}, }
Readme and manuals
projpred

The R package projpred performs the projection predictive variable selection for various regression models. Usually, the reference model will be an rstanarm or brms fit, but custom reference models can also be used. Details on supported model types are given in section “Supported types of models” of the main vignette[^1].
For details on how to cite projpred, see the projpred citation info on CRAN[^2]. Further references (including earlier work that projpred is based on) are given in section “Introduction” of the main vignette.
The vignettes[^3] illustrate how to use the projpred functions in conjunction. Details on the projpred functions as well as some shorter examples may be found in the documentation[^4].
Installation
There are two ways for installing projpred: from CRAN or from GitHub. The GitHub version might be more recent than the CRAN version, but the CRAN version might be more stable.
From CRAN
install.packages("projpred")
From GitHub
This requires the devtools package, so if necessary, the following code will also install devtools (from CRAN):
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("stan-dev/projpred", build_vignettes = TRUE)
To save time, you may omit build_vignettes = TRUE
.
[^1]: The main vignette can be accessed offline by typing
vignette(topic = "projpred", package = "projpred")
or—more
conveniently—browseVignettes("projpred")
within R.
[^2]: The citation information can be accessed offline by typing
print(citation("projpred"), bibtex = TRUE)
within R.
[^3]: The overview of all vignettes can be accessed offline by typing
browseVignettes("projpred")
within R.
[^4]: The documentation can be accessed offline using ?
or help()
within R.