monomvn-package

Estimation of multivariate normal data of arbitrary dimension
where the pattern of missing data is monotone. Through the use of
partial least squares and principal component regressions, 
where standard regressions fail,
the package can handle an (almost) arbitrary amount of missing
data.  The current version supports maximum likelihood inference.
Future versions will provide a means of sampling from a Bayesian
posterior.

package

Estimation of multivariate normal 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 an 
             (almost) arbitrary amount of missing data.  The current version 
             supports maximum likelihood inference and an alpha implementation
             of a Bayesian version employing a Bayesian lasso.  A fully 
             functional standalone (alpha) interface to the Bayesian lasso 
             (from Park & Casella) is also provided

monomvn-package: Estimation for Multivariate Normal Data with Monotone Missingness

Description

Arguments

Details

References

See Also