Analysis of Multivariate Normal Datasets with Missing Values
Changes missing value code to NA
Data augmentation for incomplete multivariate normal data
EM algorithm for incomplete normal data
Extract normal parameters from packed storage
Impute missing multivariate normal data
Observed-data loglikelihood for normal data
Observed-data log-posterior for normal data
Convert normal parameters to packed storage
Monotone data augmentation for incomplete multivariate normal data
Multiple imputation inference
Changes NA's to single precision missing value code
Random normal-inverted Wishart variate
Preliminary manipulations for a matrix of incomplete continuous data.
Initialize random number generator seed
An integrated set of functions for the analysis of multivariate normal datasets with missing values, including implementation of the EM algorithm, data augmentation, and multiple imputation.