Package: projpred 2.8.0

Frank Weber

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:Juho Piironen [aut], Markus Paasiniemi [aut], Alejandro Catalina [aut], Frank Weber [cre, aut], Aki Vehtari [aut], Jonah Gabry [ctb], Marco Colombo [ctb], Paul-Christian Bürkner [ctb], Hamada S. Badr [ctb], Brian Sullivan [ctb], Sölvi Rögnvaldsson [ctb], The LME4 Authors [cph], Yann McLatchie [ctb], Juho Timonen [ctb]

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 = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/stan-dev/projpred/issues

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

5.29 score 231 scripts 2.1k downloads 2 mentions 26 exports 59 dependencies

Last updated 11 months agofrom:157774b9ad. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 10 2024
R-4.5-linux-x86_64OKOct 10 2024

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.tabledescdistributionalfansifarvergamm4genericsggplot2gluegtablegtoolsisobandjsonlitelabelinglatticelifecyclelme4loomagrittrMASSMatrixmatrixStatsmclogitmemiscmgcvminqamunsellmvtnormnlmenloptrnnetnumDerivordinalpillarpkgconfigposteriorR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrlangrstantoolsscalestensorAtibbleucminfutf8vctrsviridisLitewithryaml

Latent projection predictive feature selection

Rendered fromlatent.Rmdusingknitr::rmarkdownon Oct 10 2024.

Last update: 2023-12-15
Started: 2023-02-12

projpred: Projection predictive feature selection

Rendered fromprojpred.Rmdusingknitr::rmarkdownon Oct 10 2024.

Last update: 2023-12-15
Started: 2022-04-01

Readme and manuals

Help Manual

Help pageTopics
Projection predictive feature selectionprojpred-package projpred
Extract projected parameter draws and coerce to 'draws_matrix' (see package 'posterior')as_draws.projection as_draws_matrix.projection
Extract projected parameter draws and coerce to matrixas.matrix.projection
Inverse-link function for augmented-data projection with binomial familyaugdat_ilink_binom
Link function for augmented-data projection with binomial familyaugdat_link_binom
Break up matrix termsbreak_up_matrix_term
Weighted averaging within clusters of parameter drawscl_agg
Ranking proportions from fold-wise predictor rankingscv_proportions cv_proportions.ranking cv_proportions.vsel
Run search and performance evaluation with cross-validationcv_varsel cv_varsel.default cv_varsel.refmodel cv_varsel.vsel
Create cross-validation foldscv-indices cvfolds cv_folds cv_ids
Binomial toy exampledf_binom
Gaussian toy exampledf_gaussian
Extend a familyextend_family
Extra family objectsextra-families Student_t
Force search termsforce_search_terms
Mesquite data setmesquite
Predictive performance resultsperformances performances.vsel performances.vselsummary
Plot ranking proportions from fold-wise predictor rankingsplot.cv_proportions plot.ranking
Plot predictive performanceplot.vsel
Predictions from a submodel (after projection)pred-projection proj_linpred proj_predict
Predictions or log posterior predictive densities from a reference modelpredict.refmodel
Predictor terms used in a 'project()' runpredictor_terms predictor_terms.projection
Print information about 'project()' outputprint.projection
Print information about a reference model objectprint.refmodel
Print results (summary) of a 'varsel()' or 'cv_varsel()' runprint.vsel
Print summary of a 'varsel()' or 'cv_varsel()' runprint.vselsummary
Projection onto submodel(s)project
Predictor ranking(s)ranking ranking.vsel
Reference model and more general informationget_refmodel get_refmodel.default get_refmodel.projection get_refmodel.refmodel get_refmodel.stanreg get_refmodel.vsel init_refmodel refmodel-init-get
Create 'cvfits' from 'cvfun'run_cvfun run_cvfun.default run_cvfun.refmodel
Retrieve the full-data solution path from a 'varsel()' or 'cv_varsel()' run or the predictor combination from a 'project()' runsolution_terms solution_terms.projection solution_terms.vsel
Suggest submodel sizesuggest_size suggest_size.vsel
Summary of a 'varsel()' or 'cv_varsel()' runsummary.vsel
Run search and performance evaluation without cross-validationvarsel varsel.default varsel.refmodel varsel.vsel
Extract response values, observation weights, and offsetsy_wobs_offs