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.
|Author||Robert B. Gramacy <firstname.lastname@example.org>|
|Date of publication||2017-01-08 15:02:13|
|Maintainer||Robert B. Gramacy <email@example.com>|
blasso: Bayesian Lasso/NG, Horseshoe, and Ridge Regression
blasso.s3: Summarizing Bayesian Lasso Output
bmonomvn: Bayesian Estimation for Multivariate Normal Data with...
cement: Hald's Cement Data
default.QP: Generating a default Quadratic Program for bmonomvn
metrics: RMSE, Expected Log Likelihood and KL Divergence Between Two...
monomvn: Maximum Likelihood Estimation for Multivariate Normal Data...
monomvn-internal: Internal Monomvn Functions
monomvn-package: Estimation for Multivariate Normal and Student-t Data with...
monomvn.s3: Summarizing monomvn output
monomvn.solve.QP: Solve a Quadratic Program
plot.monomvn: Plotting bmonomvn output
randmvn: Randomly Generate a Multivariate Normal Distribution
regress: Switch function for least squares and parsimonious monomvn...
returns: Financial Returns data from NYSE and AMEX
rmono: Randomly Impose a Monotone Missingness Pattern
rwish: Draw from the Wishart Distribution