Bayesian partial least squares regression

Fits the Bayesian partial least squares regression model introduced in Urbas et al. (2024) . Suitable for univariate and multivariate regression with high-dimensional data.

bplsr

The goal of bplsr is to …

Installation

You can install the development version of bplsr from GitHub with:

# install.packages("pak")
pak::pak("SzymonUrbas/bplsr")

Example

This is a basic example which shows you how to solve a common problem:

library(bplsr)
## basic example code

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)
#>      speed           dist       
#>  Min.   : 4.0   Min.   :  2.00  
#>  1st Qu.:12.0   1st Qu.: 26.00  
#>  Median :15.0   Median : 36.00  
#>  Mean   :15.4   Mean   : 42.98  
#>  3rd Qu.:19.0   3rd Qu.: 56.00  
#>  Max.   :25.0   Max.   :120.00

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this.

You can also embed plots, for example:

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.