a vector of observations from a MVN distribution with unknown mean and known variance-covariance.

the mean vector of the MVN prior, or a scalar constant so that the prior vector of length k with the same element repeated k times, e.g. m0 = 0

the variance-covariance matrix of the MVN prior, or the diagonal of the variance-covariance matrix of the MVN prior, or a scalar constant, say n0, so the prior is n0 * I where I is the k by k identity matrix.

the known variance covariance matrix of the data. If this value is NULL, which it is by default, then the sample covariance is used. NOTE: if this is the case then the cdf and quantile functions should really be multivariate t, but they are not - in which case the results are only (approximately) valid for large samples.

any other values to be passed to Bolstad.control

the posterior mean of the MVN posterior distribution

the posterior variance-covariance matrix of the MVN posterior distribution

a function that will evaluation the posterior cdf at a given point. This function calls mvtnmorm::pmvnorm.

a function that will find quantiles from the posterior given input probabilities. This function calls mvtnorm::qmvnorm.