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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.

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Version

Install

install.packages('projpred')

Monthly Downloads

1,863

Version

2.4.0

License

GPL-3 | file LICENSE

Maintainer

Frank Weber

Last Published

February 12th, 2023

Functions in projpred (2.4.0)

df_gaussian

Gaussian toy example
plot.vsel

Plot summary statistics of a variable selection
mesquite

Mesquite data set
predict.refmodel

Predictions or log predictive densities from a reference model
pred-projection

Predictions from a submodel (after projection)
project

Projection onto submodel(s)
print.vselsummary

Print summary of variable selection
extra-families

Extra family objects
extend_family

Extend a family
print.vsel

Print results (summary) of variable selection
do_call

Execute a function call
suggest_size

Suggest submodel size
reexports

Objects exported from other packages
varsel

Variable selection without cross-validation
refmodel-init-get

Reference model and more general information
summary.vsel

Summary statistics of a variable selection
solution_terms

Retrieve predictor solution path or predictor combination
projpred-package

Projection predictive feature selection
cv_varsel

Variable selection with cross-validation
as.matrix.projection

Extract projected parameter draws
cv-indices

Create cross-validation folds
augdat-internals

Augmented-data projection: Internals
df_binom

Binomial toy example
cl_agg

Weighted averaging within clusters of parameter draws
break_up_matrix_term

Break up matrix terms
augdat_ilink_binom

Inverse-link function for augmented-data projection with binomial family
augdat_link_binom

Link function for augmented-data projection with binomial family