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 using a nonparametric
ML approach.

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 using a nonparametric
ML approach.

c(person(given = "Reinhold", family = "Hatzinger", role = "aut"),
person(given = c("Marco", "Johannes"), family = "Maier",
email = "marco.maier@wu.ac.at", role = "cre"))
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) exploiting 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 using a non-parametric ML approach.
Reinhold Hatzinger [aut],
Marco Johannes Maier [cre]

Utilities to Fit Paired Comparison Models for Preferences
R (>= 3.0.0), stats, graphics, gnm (>= 1.0-0), colorspace
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) exploiting 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 using a non-parametric ML approach.