R: Projection Predictive Feature Selection

# Projection Predictive Feature Selection

## Documentation for package ‘projpred’ version 2.9.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

projpred-package | Projection predictive feature selection
as.matrix.projection | Extract projected parameter draws and coerce to matrix
as_draws.projection | Extract projected parameter draws and coerce to 'draws_matrix' (see package 'posterior')
as_draws_matrix.projection | Extract projected parameter draws and coerce to 'draws_matrix' (see package 'posterior')
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
break_up_matrix_term | Break up matrix terms
cl_agg | Weighted averaging within clusters of parameter draws
cv-indices | Create cross-validation folds
cvfolds | Create cross-validation folds
cv_folds | Create cross-validation folds
cv_ids | Create cross-validation folds
cv_proportions | Ranking proportions from fold-wise predictor rankings
cv_proportions.ranking | Ranking proportions from fold-wise predictor rankings
cv_proportions.vsel | Ranking proportions from fold-wise predictor rankings
cv_varsel | Run search and performance evaluation with cross-validation
cv_varsel.default | Run search and performance evaluation with cross-validation
cv_varsel.refmodel | Run search and performance evaluation with cross-validation
cv_varsel.vsel | Run search and performance evaluation with cross-validation
df_binom | Binomial toy example
df_gaussian | Gaussian toy example
extend_family | Extend a family
extra-families | Extra family objects
force_search_terms | Force search terms
get_refmodel | Reference model and more general information
get_refmodel.default | Reference model and more general information
get_refmodel.projection | Reference model and more general information
get_refmodel.refmodel | Reference model and more general information
get_refmodel.stanreg | Reference model and more general information
get_refmodel.vsel | Reference model and more general information
init_refmodel | Reference model and more general information
mesquite | Mesquite data set
performances | Predictive performance results
performances.vsel | Predictive performance results
performances.vselsummary | Predictive performance results
plot.cv_proportions | Plot ranking proportions from fold-wise predictor rankings
plot.ranking | 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 model
predictor_terms | Predictor terms used in a 'project()' run
predictor_terms.projection | 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 | Projection predictive feature selection
proj_linpred | Predictions from a submodel (after projection)
proj_predict | Predictions from a submodel (after projection)
ranking | Predictor ranking(s)
ranking.vsel | Predictor ranking(s)
refmodel-init-get | Reference model and more general information
run_cvfun | Create 'cvfits' from 'cvfun'
run_cvfun.default | Create 'cvfits' from 'cvfun'
run_cvfun.refmodel | Create 'cvfits' from 'cvfun'
solution_terms | Retrieve the full-data solution path from a 'varsel()' or 'cv_varsel()' run or the predictor combination from a 'project()' run
solution_terms.projection | Retrieve the full-data solution path from a 'varsel()' or 'cv_varsel()' run or the predictor combination from a 'project()' run
solution_terms.vsel | Retrieve the full-data solution path from a 'varsel()' or 'cv_varsel()' run or the predictor combination from a 'project()' run
Student_t | Extra family objects
suggest_size | Suggest submodel size
suggest_size.vsel | Suggest submodel size
summary.vsel | Summary of a 'varsel()' or 'cv_varsel()' run
varsel | Run search and performance evaluation without cross-validation
varsel.default | Run search and performance evaluation without cross-validation
varsel.refmodel | Run search and performance evaluation without cross-validation
varsel.vsel | Run search and performance evaluation without cross-validation
y_wobs_offs | Extract response values, observation weights, and offsets