The package support.CEs provides seven basic functions that support the implementation of choice experiments (CEs) in R: two functions for creating a CE design, which is based on orthogonal main-effect arrays; a function for converting a CE design into questionnaire format; a function for converting a CE design into a design matrix; a function for making the data set suitable for the implementation of a conditional logit model; a function for calculating the goodness-of-fit measures of an estimated model; and a function for calculating the marginal willingness to pay for the attributes and/or levels of the estimated model
Johanna Mollerstrom, Bjørn-Atle Reme, Erik Ø. Sørensen, Luck, choice and responsibility — An experim...
At the time of creating an experimental design for a stated choice experiment, the analyst often doe...
Modelling stated preferences is an almost mystical science and as there is no data explaining how th...
The package support.CEs provides seven basic functions that support the implementation of choice exp...
This provides R functions to aid analysis of selection. They were written fairly generically so shou...
This paper introduces mixl , a new R package for the estimation of advanced choice models. The estim...
Efficient experimental designs offer the potential to reduce confidence intervals for parameters of ...
Efficient experimental designs offer the potential to reduce confidence intervals for parameters of ...
International audienceThis paper introduces two R packages available on the Comprehensive R Archive ...
Monte Carlo simulation studies play an important role in operational and academic research in educat...
AbstractStated-preference methods are a class of evaluation techniques for studying the preferences ...
This paper introduces two R packages available on the Comprehensive R Archive network. The main appl...
The key tasks in the design of a choice modelling (CM) experiment are to define the scope, scale and...
This R-code is intended for multicriteria decision support problems solved by the Composition of Pro...
This chapter focuses on stated preferences obtained from discrete choice experiments (DCEs also know...
Johanna Mollerstrom, Bjørn-Atle Reme, Erik Ø. Sørensen, Luck, choice and responsibility — An experim...
At the time of creating an experimental design for a stated choice experiment, the analyst often doe...
Modelling stated preferences is an almost mystical science and as there is no data explaining how th...
The package support.CEs provides seven basic functions that support the implementation of choice exp...
This provides R functions to aid analysis of selection. They were written fairly generically so shou...
This paper introduces mixl , a new R package for the estimation of advanced choice models. The estim...
Efficient experimental designs offer the potential to reduce confidence intervals for parameters of ...
Efficient experimental designs offer the potential to reduce confidence intervals for parameters of ...
International audienceThis paper introduces two R packages available on the Comprehensive R Archive ...
Monte Carlo simulation studies play an important role in operational and academic research in educat...
AbstractStated-preference methods are a class of evaluation techniques for studying the preferences ...
This paper introduces two R packages available on the Comprehensive R Archive network. The main appl...
The key tasks in the design of a choice modelling (CM) experiment are to define the scope, scale and...
This R-code is intended for multicriteria decision support problems solved by the Composition of Pro...
This chapter focuses on stated preferences obtained from discrete choice experiments (DCEs also know...
Johanna Mollerstrom, Bjørn-Atle Reme, Erik Ø. Sørensen, Luck, choice and responsibility — An experim...
At the time of creating an experimental design for a stated choice experiment, the analyst often doe...
Modelling stated preferences is an almost mystical science and as there is no data explaining how th...