How to estimate mixed logit (or random parameter) discrete choice models in R I have designed a discrete choice experiment to estimate WTP figures for a non-market good within a group where I think preference is heterogeneous. But I am currently struggling to estimate the model.
I have read Train's "DCE Methods with Simulation", but I am unable to implement the programming techniques he proposed for estimation of model parameters. I don't have a programming background, but I can read and implement R or Stata command syntax. I have tried to read around but it's not helping. Most of the resources I found were on multinomial logit.
I would be glad if someone can recommend a go-to R-Package for mixed logit models and a book or set of videos that gives a step-by-step guide on how to do this practically.
 A: there are several packages that you can use in R for mixed logit estimation. Some use frequentists approaches based on simulated ML, others Bayesian approaches based on Markov Chains.
**** R packages based on S-ML
1) mlogit
2) gmnl
**** R packages based on Bayesian estimation
1) RSGHB
https://github.com/RSGInc/RSGHB_Examples
https://cran.r-project.org/web/packages/RSGHB/vignettes/RSGHB_HowTo.pdf
https://cran.r-project.org/web/packages/RSGHB/RSGHB.pdf
http://help.statwizards.com/data-wizard/statistics_programs/r_-_rsghb_package.htm
2) RStan based:
https://rawgit.com/rtrangucci/class_20170809/master/multinomial-logit/multinomial-logit-regression.html
there was also a code based on mlogit by Daniel Guhl posted somewhere, which I found easy to implement at the time, but cannot find at the minute. 
************ Stata the major players here are 
1) Hong Il Yoo (Durham Business School for packages lclogit and lclogitml) and
2) especially Arne Hole (Sheffield for packages mixedlogit and mixedlogitwtp to get models in WTP-space as per Scarpa, Thiene and Train AJAE 2008, Train & Weeks 2005). Look them up on the web. 
Good luck
