extract_unparsed_values

If the utility function contains parameters that are dummy coded, the dummy
coding is handled here. By expanding the dummy coding prior to parsing we can
directly consider Bayesian priors for each level.

Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163).

Erlend Dancke Sandorf

spdesign

Designing Stated Preference Experiments

Danny Campbell

extract_unparsed_values function

<dl><dt>string</dt>
<dd>A character string</dd></dl>

Arguments

Extract unparsed named values of the utilitiy function — extract_unparsed_values

<dl>

<dt>string</dt>
<dd>A character string</dd>

</dl>

extract_unparsed_values: Extract unparsed named values of the utilitiy function

Description

Usage

Value

Arguments