rwish

Wishart

Random generation from the Wishart distribution.

distribution

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:10.48550/arXiv.0907.2135>.
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.

Robert Gramacy

monomvn

Estimation for MVN and Student-t Data with Monotone Missingness

rwish function

<dl> <dt>v</dt>
<dd>degrees of freedom (scalar)</dd> <dt>S</dt>
<dd>inverse scale matrix \((p \times p)\)</dd></dl>

Arguments

Draw from the Wishart Distribution — rwish

<dl>

 <dt>v</dt>
<dd>degrees of freedom (scalar)</dd>

 <dt>S</dt>
<dd>inverse scale matrix \((p \times p)\)</dd>

</dl>

rwish: Draw from the Wishart Distribution

Description

Usage

Value

Arguments

Details

References

Examples