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]</code>) parameters to a
    Beta$(\alpha, \beta)$ distribution
    describing the proportion of <code>NA</code> entries in each column.
    The default setting <code>ab = NULL</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

datagen

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

rmono: Randomly Impose a Monotone Missingness Pattern

Description

Usage

Arguments

Value

Details

References

See Also

Examples