The **projpred** R package performs the projection
predictive variable selection for generalized linear and additive models
as well as for generalized linear and additive multilevel models (with
the support for additive models being still experimental). The package
is compatible with the **rstanarm** and
**brms**
packages, but custom reference models can also be used.

The projection predictive variable selection is based on the ideas of
Goutis and Robert (1998) and Dupuis and Robert (2003). The methods
implemented in **projpred** are described in detail in
Piironen et al. (2020) and Catalina et al. (2020). They are evaluated in
comparison to many other methods in Piironen and Vehtari (2017). Type
`citation("projpred")`

in R (or see the `CITATION`

file) for details on how to cite **projpred**.

Currently, the supported response distributions (objects of class
`family`

in R) are `gaussian()`

,
`binomial()`

(via the **brms** package,
`brms::bernoulli()`

is also supported), and
`poisson()`

.

The vignettes
(currently, there is only a single one) 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.

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.

`install.packages("projpred")`

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")
}::install_github("stan-dev/projpred", build_vignettes = TRUE) devtools
```

To save time, you may omit `build_vignettes = TRUE`

.

Catalina, A., Bürkner, P.-C., and Vehtari, A. (2020). Projection
predictive inference for generalized linear and additive multilevel
models. *arXiv:2010.06994*. URL: https://arxiv.org/abs/2010.06994.

Dupuis, J. A. and Robert, C. P. (2003). Variable selection in
qualitative models via an entropic explanatory power. *Journal of
Statistical Planning and Inference*,
**111**(1-2):77–94. DOI: 10.1016/S0378-3758(02)00286-0.

Goutis, C. and Robert, C. P. (1998). Model choice in generalised
linear models: A Bayesian approach via Kullback–Leibler projections.
*Biometrika*, **85**(1):29–37.

Piironen, J. and 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.

Piironen, J., Paasiniemi, M., and 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.