update_m

gibbs_sampler

Bayes estimation of probit choice models in cross-sectional and panel
settings. The package can analyze binary, multivariate, ordered, and ranked
choices, as well as heterogeneity of choice behavior among deciders. The
main functionality includes model fitting via Gibbs sampling, tools for
convergence diagnostic, choice data simulation, in-sample and out-of-sample
choice prediction, and model selection using information criteria and Bayes
factors. The latent class model extension facilitates preference-based
decider classification, where the number of latent classes can be inferred
via the Dirichlet process or a weight-based updating heuristic. This allows
for flexible modeling of choice behavior without the need to impose
structural constraints. For a reference on the method, see Oelschlaeger and
Bauer (2021) <https://trid.trb.org/view/1759753>.

Lennart Oelschläger

RprobitB

Bayesian Probit Choice Modeling

Dietmar Bauer

update_m function

<dl><dt>C</dt>
<dd>[<code>integer(1)</code>] 
The number (greater or equal 1) of latent classes of decision makers.</dd>
<dt>z</dt>
<dd>[<code>numeric(N)</code>] 
The decider class allocations.</dd>
<dt>non_zero</dt>
<dd>[<code>logical(1)</code>] 
Enforce strictly positive values in <code>m</code> (for numerical stability)?</dd></dl>

Arguments

Update class sizes — update_m

<dl>

<dt>C</dt>
<dd>[<code>integer(1)</code>] 
The number (greater or equal 1) of latent classes of decision makers.</dd>


<dt>z</dt>
<dd>[<code>numeric(N)</code>] 
The decider class allocations.</dd>


<dt>non_zero</dt>
<dd>[<code>logical(1)</code>] 
Enforce strictly positive values in <code>m</code> (for numerical stability)?</dd>

</dl>

update_m: Update class sizes

Description

Usage

Value

Arguments

Examples