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 the lasso (double-exponential) and Normal-Gamma priors, and
Student-t errors.  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), and ridge
regression with model selection via Reversible Jump, and
student-t errors (from Geweke) is also provided

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 the lasso (double-exponential) and
Normal-Gamma priors, and Student-t errors.  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), and ridge regression with model selection via
Reversible Jump, and student-t errors (from Geweke) is also
provided

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 the
lasso (double-exponential) and Normal-Gamma priors,
and Student-t errors.  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),
and ridge regression with model selection via Reversible Jump,
and student-t errors (from Geweke) is also provided