Wiener

dwiener

rwiener

alpha

beta

delta

Response: <code>"upper"</code> or <code>"lower"</code>. 
If no character vector, it is coerced to logical 
where <code>TRUE</code> indicates <code>"upper"</code> and 
<code>FALSE</code> indicates <code>"lower"</code>.

resp

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

Number of samples to draw from the distribution.

Which types of responses to return? By default,
return both the response times <code>"q"</code> and the dichotomous 
responses <code>"resp"</code>. If either <code>"q"</code> or <code>"resp"</code>, 
return only one of the two types.

types

Density function and random generation for the Wiener
diffusion model distribution with boundary separation <code>alpha</code>,
non-decision time <code>tau</code>, bias <code>beta</code> and 
drift rate <code>delta</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>.

Wiener: The Wiener Diffusion Model Distribution

Description

Usage

Arguments

Details

See Also