rmono

minimum number of non-<code>NA</code> entries in each column

a two-vector of $\alpha$ (<code>ab[1]</code>) and
    $\beta$
    (<code><ab>[2]</ab>) parameters to a beta distribution describing the
      proportion of <code>NA</code> entries in each column.  The default
      setting<code>ab = NULL</code> yields a u</code>

Randomly impose a monotone missingness pattern by replacing the ends
  of each column of the input matrix by a random number of <code>NA</code>s

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

rmono: Randomly Impose a Monotone Missingness Pattern

Description

Usage

Arguments