An R package implementing model-based sliced inverse regression as described in Scrucca (2011).

Model-based Sliced Inverse Regression (MSIR) is a dimension reduction method based on Gaussian finite mixture models which provides an extension to sliced inverse regression (SIR).

The basis of the MSIR subspace is estimated by modeling the inverse distribution within slice using Gaussian finite mixtures with number of components and covariance matrix parameterization selected by BIC or defined by the user.


You can install the released version of msir from CRAN using:


or the development version from GitHub:

# install.packages("devtools")
devtools::install_github("luca-scr/msir", build = TRUE, build_opts = c("--no-resave-data", "--no-manual"))


Usage of the main function and some examples are included in vignette A quick tour of msir, which is available as


The vignette is also available in the Vignette section on the navigation bar on top of the package’s web page.


Scrucca, L. (2011) Model-based SIR for dimension reduction. Computational Statistics & Data Analysis, 55(11), 3010-3026.