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 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 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