projpred2.8.0 package

Projection Predictive Feature Selection

as.matrix.projection

Extract projected parameter draws and coerce to matrix

as_draws_matrix.projection

Extract projected parameter draws and coerce to draws_matrix (see pa...

augdat-internals

Augmented-data projection: Internals

augdat_ilink_binom

Inverse-link function for augmented-data projection with binomial fami...

augdat_link_binom

Link function for augmented-data projection with binomial family

break_up_matrix_term

Break up matrix terms

cl_agg

Weighted averaging within clusters of parameter draws

cv-indices

Create cross-validation folds

cv_proportions

Ranking proportions from fold-wise predictor rankings

cv_varsel

Run search and performance evaluation with cross-validation

do_call

Execute a function call

extend_family

Extend a family

extra-families

Extra family objects

force_search_terms

Force search terms

performances

Predictive performance results

plot.cv_proportions

Plot ranking proportions from fold-wise predictor rankings

plot.vsel

Plot predictive performance

pred-projection

Predictions from a submodel (after projection)

predict.refmodel

Predictions or log posterior predictive densities from a reference mod...

predictor_terms

Predictor terms used in a project() run

print.projection

Print information about project() output

print.refmodel

Print information about a reference model object

print.vsel

Print results (summary) of a varsel() or cv_varsel() run

print.vselsummary

Print summary of a varsel() or cv_varsel() run

project

Projection onto submodel(s)

projpred-package

Projection predictive feature selection

ranking

Predictor ranking(s)

refmodel-init-get

Reference model and more general information

run_cvfun

Create cvfits from cvfun

solution_terms

Retrieve the full-data solution path from a varsel() or `cv_varsel()...

suggest_size

Suggest submodel size

summary.vsel

Summary of a varsel() or cv_varsel() run

varsel

Run search and performance evaluation without cross-validation

y_wobs_offs

Extract response values, observation weights, and offsets

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.

  • Maintainer: Frank Weber
  • License: GPL-3 | file LICENSE
  • Last published: 2023-12-15