Efficient Designs for Discrete Choice Experiments
person("Frits", "Traets", email = "frits.traets@kuleuven.be", role = c("aut", "cre"))
Generates efficient designs for discrete choice experiments based on the multinomial logit model,
and individually adapted designs for the mixed multinomial logit model. Crabbe M, Akinc D and Vandebroek M (2014)
<doi:10.1016/j.trb.2013.11.008>.
dplyr, maxLik, gtools, MASS, stats, scales

dplyr, maxLik, gtools, stats, MASS, scales

dplyr, maxLik, gtools, MASS, stats, scales, Rdpack

Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Crabbe M, Akinc D and Vandebroek M (2014) <doi:10.1016/j.trb.2013.11.008>.
dplyr, gtools, MASS, maxLik, Rdpack, stats, scales, utils

dplyr, gtools, MASS, maxLik, mlogit, parallel, Rcpp (>=
0.12.18), Rdpack, stats, scales, tmvtnorm, utils

c(
person("Frits", "Traets", email = "frits.traets@kuleuven.be", role = c("aut", "cre")),
person("Daniel", "Gil", email = "danielgils@gmail.com", role = "ctb"))
dplyr, MASS, mlogit, parallel, Rcpp (>= 0.12.18), Rdpack,
stats, scales, tmvtnorm, utils
Frits Traets [aut, cre],
Daniel Gil [ctb]

Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) <doi:10.18637/jss.v096.i03>.

dplyr, MASS, parallel, Rcpp (>= 0.12.18), Rdpack, stats,
scales, tmvtnorm, utils, dfidx

Inference in Randomized Controlled Trials with Death and
Missingness
Chenguang Wang [aut, cre],
Andrew Leroux [aut, cre],
Elizabeth Colantuoni [aut],
Daniel O Scharfstein [aut]
In randomized studies involving severely ill patients, functional
outcomes are often unobserved due to missed clinic visits, premature
withdrawal or death. It is well known that if these unobserved functional
outcomes are not handled properly, biased treatment comparisons can be
produced. In this package, we implement a procedure for comparing treatments
that is based on the composite endpoint of both the functional outcome and
survival. The procedure considers missing data imputation with a sensitivity
analysis strategy to handle the unobserved functional outcomes not due to
death.