metrics
(Package: monomvn) :
RMSE, Expected Log Likelihood and KL Divergence Between
These functions calculate the root-mean-squared-error, the expected log likelihood, and Kullback-Leibler (KL) divergence (a.k.a. distance), between two multivariate normal (MVN) distributions described by their mean vector and covariance matrix
Summarizing, printing, and plotting the contents of a "blasso"-class object containing samples from the posterior distribution of a Bayesian lasso model
bmonomvn
(Package: monomvn) :
Bayesian Estimation for Multivariate Normal Data with
Bayesian estimation via sampling from the posterior distribution of the of the mean and covariance matrix of multivariate normal (MVN) distributed data with a monotone missingness pattern, via Gibbs Sampling. Through the use of parsimonious/shrinkage regressions (lasso/NG & ridge), where standard regressions fail, this function can handle an (almost) arbitrary amount of missing data
randmvn
(Package: monomvn) :
Randomly Generate a Multivariate Normal Distribution
Randomly generate a mean vector and covariance matrix describing a multivariate normal (MVN) distribution, and then sample from it
● Data Source:
CranContrib
● Keywords: datagen, distribution
● Alias: randmvn
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0 images
monomvn-package
(Package: monomvn) :
Estimation for Multivariate Normal and Student-t Data with Monotone Missingness
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), the 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
Functions for visualizing the output from bmonomvn, particularly the posterior standard deviation estimates of the mean vector and covariance matrix, and samples from the solution to a Quadratic Program
monomvn
(Package: monomvn) :
Maximum Likelihood Estimation for Multivariate Normal
Maximum likelihood estimation of the mean and covariance matrix of multivariate normal (MVN) distributed data with a monotone missingness pattern. Through the use of parsimonious/shrinkage regressions (e.g., plsr, pcr, ridge, lasso, etc.), where standard regressions fail, this function can handle an (almost) arbitrary amount of missing data
providing 
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