add_constants

Augments parameter matrix or vector p with constant parameters (also used in data)

Fit Bayesian (hierarchical) cognitive models
using a linear modeling language interface using particle metropolis Markov
chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM),
linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal
race model (LNR) are supported. Additionally, users can specify their own likelihood
function and/or choose for non-hierarchical
estimation, as well as for a diagonal, blocked or full multivariate normal
group-level distribution to test individual differences. Prior specification
is facilitated through methods that visualize the (implied) prior.
A wide range of plotting functions assist in assessing model convergence and
posterior inference. Models can be easily evaluated using functions
that plot posterior predictions or using relative model comparison metrics
such as information criteria or Bayes factors.
References: Stevenson et al. (2024) <doi:10.31234/osf.io/2e4dq>.

Niek Stevenson

EMC2

Bayesian Hierarchical Analysis of Cognitive Models of Choice

Michelle Donzallaz

Andrew Heathcote

Steven Miletić

Jochen Voss

Andreas Voss

add_constants function

<dl><dt>p</dt>
<dd>either a matrix or vector of parameters</dd>
<dt>constants</dt>
<dd>a named vector of constants</dd></dl>

Arguments

Augments parameter matrix or vector p with constant parameters (also used in data) — add_constants

<dl>

<dt>p</dt>
<dd>either a matrix or vector of parameters</dd>


<dt>constants</dt>
<dd>a named vector of constants</dd>

</dl>

add_constants: Augments parameter matrix or vector p with constant parameters (also used in data)

Description

Usage

Value

Arguments