Hypothesis testing of the parameters of multivariate normal distributions, including the testing of a single mean vector, two mean vectors, multiple mean vectors, a single covariance matrix, multiple covariance matrices, a mean and a covariance matrix simultaneously, and the testing of independence of multivariate normal random vectors. Huixuan, Gao (2005, ISBN:9787301078587), "Applied Multivariate Statistical Analysis".

Fits the Bayesian multinomial probit model via Markov chain
 Monte Carlo.  The multinomial probit model is often used to analyze 
 the discrete choices made by individuals recorded in survey data. 
 Examples where the multinomial probit model may be useful include the 
 analysis of product choice by consumers in market research and the 
 analysis of candidate or party choice by voters in electoral studies.  
 The MNP package can also fit the model with different choice sets for 
 each individual, and complete or partial individual choice orderings 
 of the available alternatives from the choice set. The estimation is
 based on the efficient marginal data augmentation algorithm that is 
 developed by Imai and van Dyk (2005). "A Bayesian Analysis of the 
 Multinomial Probit Model Using the Data Augmentation." Journal of 
 Econometrics, Vol. 124, No. 2 (February), pp. 311-334. 
 <doi:10.1016/j.jeconom.2004.02.002>  Detailed examples are given in 
 Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial 
 Probit Model."  Journal of Statistical Software, Vol. 14, No. 3 (May), 
 pp. 1-32. <doi:10.18637/jss.v014.i03>.

An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.