Multivariate normal inference under monotone missingness
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.
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
Rfrom cran.r-project.org by selecting the version for your operating system.
- Install the
larspackages, from within
R> install.packages(c("monomvn", "pls", "lars"))
- Optionally, install the
R> install.packages(c("mvtnorm", "accuracy"))
- Load the library as you would for any
- See the package documentation.
version of the reference manual, or help pages, is also available.
The help pages can be accessed from within
R. Try starting with:
R> ?monomvn # follow the examples
R> ?bmonomvn # for a Bayesian version
R> ?blasso # for Bayesian lasso regression
- Gramacy, R.B., Pantaleo, E. (2009). Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing. Bayesian Analysis 5(2), pp. 237-262; preprint on arXiv:0907.2135
- Gramacy, R.B., Lee JH. (2007). On estimating covariances between many assets with histories of highly variable length. arXiv:0710.5837
- Roderick J.A. Little and Donald B. Rubin (2002). Statistical Analysis with Missing Data, Second Edition. Wilely.
- Bjorn-Helge Mevik and Ron Wehrens (2007).
plsPackage: Principal Component and Partial Least Squares Regression in
R. Journal of Statistical Software 18(2)
- Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2003). Least Angle Regression (with discussion). Annals of Statistics 32(2)
- Park, T., Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association 103(482), pp. 681-686(6)
- Griffin, J.E., Brown, P.J. (2009) Inference with Normal-Gamma prior distributions in regression problems. Bayesian Analysis, 5(1), pp. 171-188
- Carvalho, C.M., Polson, N.G., and Scott, J.G. (2010) The horseshoe estimator for sparse signals. Biometrika 97(2): pp. 465-480.
- Geweke, J. (1996). Variable selection and model comparison in regression. In Bayesian Statistics 5. Editors: J.M. Bernardo, J.O. Berger, A.P. Dawid and A.F.M. Smith, 609-620. Oxford Press.
- Trevor Hastie, Robert Tibshirani and Jerome Friedman (2002). Elements of Statistical Learning. Springer, NY.
- Some of the code for
monomvn, and its subroutines, was inspired by code written by Daniel Heitjan.