MultiStudentT

dmulti_student_t

rmulti_student_t

Vector or matrix of quantiles. If <code>x</code> is a matrix, 
each row is taken to be a quantile.

Location vector with length equal to the number of dimensions.

Sigma

Logical; If <code>TRUE</code>, values are returned on the log scale.

Logical; Indicates whether several input checks
should be performed. Defaults to <code>FALSE</code> to improve
efficiency.

check

Number of samples to draw from the distribution.

Density function and random generation for the multivariate Student-t
distribution with location vector <code>mu</code>, covariance matrix <code>Sigma</code>,
and degrees of freedom <code>df</code>.

Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others --
linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include non-linear and
smooth terms, auto-correlation structures, censored data, meta-analytic
standard errors, and quite a few more. In addition, all parameters of the
response distribution can be predicted in order to perform distributional
regression. Prior specifications are flexible and explicitly encourage
users to apply prior distributions that actually reflect their beliefs.
Model fit can easily be assessed and compared with posterior predictive
checks and leave-one-out cross-validation. References: B<c3><bc>rkner (2017)
<doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

MultiStudentT: The Multivariate Student-t Distribution

Description

Usage

Arguments

Details