student_t

half_t

Student-t and half-t priors. Note that this can be used to represent an
effectively normal distribution prior by setting the first argument (the
degrees of freedom parameter) to a large value (roughly 50 or above).

Implements Bayesian spatial and spatiotemporal
models that optionally allow for extreme spatial deviations through
time. 'glmmfields' uses a predictive process approach with random
fields implemented through a multivariate-t distribution instead of
the usual multivariate normal. Sampling is conducted with 'Stan'.
References: Anderson and Ward (2019) <doi:10.1002/ecy.2403>.

Sean Anderson

glmmfields

Generalized Linear Mixed Models with Robust Random Fields for
Spatiotemporal Modeling

Trustees of Columbia University

student_t function

<dl><dt>df</dt>
<dd>Degrees of freedom parameter</dd>
<dt>location</dt>
<dd>Location parameter</dd>
<dt>scale</dt>
<dd>Scale parameter</dd></dl>

Arguments

Student-t and half-t priors — student_t

<dl>

<dt>df</dt>
<dd>Degrees of freedom parameter</dd>


<dt>location</dt>
<dd>Location parameter</dd>


<dt>scale</dt>
<dd>Scale parameter</dd>

</dl>

student_t: Student-t and half-t priors

Description

Usage

Arguments

Examples