Robert B. Gramacy Associate Professor of Econometrics and Statistics

Multivariate normal inference under monotone missingness

monomvn is an R package for estimation of multivariate normal and Student-t data of arbitrary dimension where the pattern of missing data is monotone. Through the use of parsimonious/shrinkage regressions (plsr, pcr, lasso, ridge, etc.), where standard regressions fail, the package can handle a nearly arbitrary amount of missing data.

This software is licensed under the GNU Lesser Public License (LGPL), version 2 or later. See the change log and an archive of previous versions.

The current version provides:

  • maximum likelihood inference with optional penalties such as ridge, lasso, partial least squares, principal components, etc.
  • Bayesian inference employing scale-mixture data augmentation
  • A fully functional standalone interface to the Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown), Horseshoe (from Carvalho, Polson, & Scott), and ridge regression with model selection via Reversible Jump, and student-t errors (from Geweke).
  • Monotone data augmentation extends the Bayesian approach to arbitrary missingness patterns.

Obtaining the package

  • Download R from by selecting the version for your operating system.
  • Install the monomvn, pls and lars packages, from within R.
    R> install.packages(c("monomvn", "pls", "lars"))
  • Optionally, install the mvtnorm and accuracy packages.
    R> install.packages(c("mvtnorm", "accuracy"))
  • Load the library as you would for any R library.
    R> library(monomvn)


  • See the package documentation. A pdf version of the reference manual, or help pages, is also available. The help pages can be accessed from within R. Try starting with:
      R> help(package=monomvn)
      R> ?monomvn    # follow the examples
      R> ?bmonomvn   # for a Bayesian version
      R> ?blasso     # for Bayesian lasso regression