Description Usage Arguments Value Examples
rtmvt
simulates truncated multivariate (p-dimensional) Student's t distribution subject to linear inequality constraints. The constraints should be written as a matrix (D
) with lower
and upper
as the lower and upper bounds for those constraints respectively. Note that D
can be non-full rank, which generalizes many traditional methods.
1 |
n |
number of random samples desired (sample size). |
Mean |
location vector of the multivariate Student's t distribution. |
Sigma |
positive definite dispersion matrix of the multivariate t distribution. |
nu |
degrees of freedom for Student-t distribution. |
D |
matrix or vector of coefficients of linear inequality constraints. |
lower |
lower bound vector for truncation. |
upper |
upper bound vector for truncation. |
int |
initial value vector for Gibbs sampler (satisfying truncation), if |
burn |
burn-in iterations discarded (default as |
thin |
thinning lag (default as |
rtmvt
returns a (n*p
) matrix (or vector when n=1
) containing random numbers which follows truncated multivariate Student-t distribution.
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