Estimation for MVN and Student-t Data with Monotone Missingness
RMSE, Expected Log Likelihood and KL Divergence Between Two Multivaria...
Internal Monomvn Functions
Estimation for Multivariate Normal and Student-t Data with Monotone Mi...
Maximum Likelihood Estimation for Multivariate Normal Data with Monoto...
Bayesian Lasso/NG, Horseshoe, and Ridge Regression
Summarizing Bayesian Lasso Output
Bayesian Estimation for Multivariate Normal Data with Monotone Missing...
Generating a default Quadratic Program for bmonomvn
Summarizing monomvn output
Solve a Quadratic Program
Plotting bmonomvn output
Randomly Generate a Multivariate Normal Distribution
Switch function for least squares and parsimonious monomvn regressions
Randomly Impose a Monotone Missingness Pattern
Draw from the Wishart 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) <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.