monomvn.solve.QP

a positive-definite covariance <code>matrix</code> whose
 dimensions agree with the Quadratic Program, e.g.,
 <code>nrow(QP$Amat)</code>

a Quadratic Programming object like one that can
 be generated automatically by <code>default.QP</code>

an mean vector with
 <code>length(mu) = nrow(QP$Amat)</code> that is required
 if <code>QP$dmu == TRUE</code> or <code>QP$mu.constr[1] != 0</code>

Solve a Quadratic Program specified by a <code>QP</code> object
 using the covariance matrix and mean vector specified

 optimize 

Estimation of multivariate normal (MVN) and student-t data of
arbitrary dimension where the pattern of missing data is monotone.
See Pantaleo and Gramacy (2010) <doi:10.1214/10-BA602>.
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 supports maximum likelihood inference and
a full Bayesian approach employing scale-mixtures for Gibbs sampling.
Monotone data augmentation extends this Bayesian approach to arbitrary
missingness patterns. 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) is also provided.

monomvn.solve.QP: Solve a Quadratic Program

Description

Usage

Arguments

Value

Details

See Also