Designing Stated Preference Experiments
S-error
Cleans the utility expression
Generic for extracting the vector of priors
Check whether the utility function contains dummy coded variables
Correlation
Cycling of attribute levels
Define base x_j
Extract all names
Extract attribute names
Extract distributions
Extract the frequency of levels
Extracts the named values of the utility function
Extract the parameter distribution
Extract parameter names
Extract the prior distribution
Extract specified
Extract unparsed named values of the utilitiy function
Extract the value argument(s)
Find a design using a modified Federov algorithm
Test whether a design candidate fits the constraints imposed by the le...
Generate the full factorial
Creates a printable version of the efficiency criteria
Prints the initial header for the table of results
Relabeling of attribute levels
Removes all brackets
Removes the parameter from the utility string
Remove round bracket
Remove square bracket
Remove all white spaces
Repeat columns
Repeat rows
Extract the variance co-variance matrix
Generic for getting the attributes and levels from the utility functio...
Generic for getting the attribute names
Transform to the lognormal distribution
Transform to the normal distribution
Transform to the triangular distribution
Transform to the uniform distribution
Update the utility function
Create formulas from the utility functions
Expand the list of attributes and levels to the "wide" format
Check whether all priors and attributes have specified levels
Check whether any priors or attributes are specified with a value more...
Check whether we can achieve attribute level balance
Block the design
A-error
C-error
D-error
Calculate efficiency criteria
Calculate efficiency
Make pseudo random draws
Define x_j
Derive the variance covariance matrix for the MNL model
Derive the variance covariance matrix for the RPL model
Derive the variance covariance matrix of the design
Expand the sequence of integers
Print package startup message
Find the position of the dummy coded attributes
Evaluate the design candidate
Exclude rows from the candidate set
Generate an efficient experimental design
Generates a candidate for the RSC algorithm
Tests whether the utility expression contains Bayesian priors
Tests whether the utility expression contains random parameters
Tests whether a utility function is balanced
Print level balance of your design
Attribute level occurrence lookup tables
Make random draws
Make Modified Latin Hypercube Draws
Make scrambled Halton draws
Make scrambled sobol draws
Wrapper for halton()
Make sobol draws
Find minimum level occurrences
Find the number of levels
Evaluating a distribution
Extract or set attribute level occurrences
Prepare the list of priors
Prints iteration information
A generic function for printing an 'spdesign' object
Generic for extracting the vector of priors
Calculate the MNL probabilities
Calculate the probabilities of the design
Compute the radical inverse
Create a random design_object candidate
Make a random design
Objects exported from other packages
Check if the design is too small
Make a design candidate based on the rsc algorithm
Sets the default level occurrence in an attribute level balanced desig...
Validate design opt
Shuffle the order of points in the unit interval.
spdesign: Designing Stated Preference Experiments
Create a summary of the experimental design
Swapping of attribute
Transform distribution
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).