2-Stage Preference Trial Design and Analysis

c(
person(given="Briana",
family="Cameron",
email="briana.cameron@yale.edu",
role=c("aut", "cph")),
person(given="Denise",
family="Esserman",
email="denise.esserman@yale.edu", role="ctb"),
person(given="Michael",
family="Kane",
email="michael.kane@yale.edu",
role=c("cre", "ctb"),
comment = c(ORCID = "http://orcid.org/0000-0003-1899-6662")))
Briana Cameron [aut, cph],
Denise Esserman [ctb],
Michael Kane [cre, ctb] (<https://orcid.org/0000-0003-1899-6662>)

Design and analyze two-stage randomized trials with a continuous
outcome measure. The package contains functions to compute the required
sample size needed to detect a given preference, treatment, and selection
effect; alternatively, the package contains functions that can report the
study power given a fixed sample size. Finally, analysis functions are
provided to test each effect using either summary data (i.e. means,
variances) or raw study data <doi:10.18637/jss.v094.c02>.

Utilities to fit paired comparison models for preferences
Generates design matrix for analysing real paired
comparisons and derived paired comparison data
(Likert type items / ratings or rankings) using a
loglinear approach.  Fits loglinear Bradley-Terry
model (LLBT) exploting an eliminate feature.
Computes pattern models for paired comparisons,
rankings, and ratings.  Some treatment of missing
values (MCAR and MNAR).

Generates design matrix for analysing real paired
comparisons and derived paired comparison data (Likert type
items / ratings or rankings) using a loglinear approach.  Fits
loglinear Bradley-Terry model (LLBT) exploting an eliminate
feature. Computes pattern models for paired comparisons,
rankings, and ratings.  Some treatment of missing values (MCAR
and MNAR).

Generates design matrix for analysing real paired
comparisons and derived paired comparison data (Likert type
items / ratings or rankings) using a loglinear approach.  Fits
loglinear Bradley-Terry model (LLBT) exploting an eliminate
feature. Computes pattern models for paired comparisons,
rankings, and ratings.  Some treatment of missing values (MCAR
and MNAR). Fits latent class (mixture) models for paired
comparison, rating and ranking patterns.