Modelling comparison data with ordinal response
This package extends the Bradley-Terry-Luce model and is able to
fit pair comparison models with an ordinal response. It is also possible to
incorporate an order effect or, equivalently, a home advantage.

This package extends the Bradley-Terry-Luce model for fitting pair
comparison models with an ordinal response. It is also possible to
incorporate an order effect, or, equivalently, an effect for the home
advantage.
BradleyTerry2, prefmod, psychotree, psychotools, psychomix
'ordBTL.R' 'design.R' 'wide2long.R' 'getConstr.R' 'getRank.R'

Separating Location and Dispersion in Ordinal Regression Models
Estimate location-shift models or rating-scale models accounting for response styles (RSRS) for the regression analysis of ordinal responses.

Selection and/or Smoothing of Ordinal Predictors
Selection and/or smoothing of ordinally scaled independent
variables using a Group Lasso or generalized Ridge penalty

Selection and/or smoothing of ordinally scaled independent
variables using a group lasso or generalized ridge penalty

Selection and/or smoothing of ordinally scaled independent variables using a group lasso or generalized ridge penalty.

Selection, Fusion, Smoothing and Principal Components Analysis
for Ordinal Variables
c(
person("Jan", "Gertheiss", email = "jan.gertheiss@hsu-hh.de", role = "aut"),
person("Aisouda", "Hoshiyar", email = "aisouda.hoshiyar@hsu-hh.de", role = c("aut","cre")),
person("Fabian", "Scheipl", email = "fabian.scheipl@stat.uni-muenchen.de", role = "ctb"))
Selection, fusion, and/or smoothing of ordinally scaled independent variables using a group lasso, fused lasso or generalized ridge penalty, as well as non-linear principal components analysis for ordinal variables using a second-order difference/smoothing penalty.
grplasso, mgcv, RLRsim, quadprog, glmpath
Jan Gertheiss [aut],
Aisouda Hoshiyar [aut, cre],
Fabian Scheipl [ctb]

Ordered Beta Regression Models with Brms
person(
"Robert", "Kubinec",
email = "rmk7@nyu.edu",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-6655-4119"))
Implements ordered beta regression models, which are for modeling continuous variables with upper and lower bounds, such as
survey sliders, dose-response relationships and indexes. For more information, see
Kubinec (2022) <doi:10.31235/osf.io/2sx6y>. The package is a front-end to the R package 'brms', which
facilitates a range of regression specifications, including hierarchical, dynamic and
multivariate modeling.
R (>= 3.5), faux, dplyr, brms, tidyr, stats
rmarkdown, knitr, ggplot2, modelsummary, marginaleffects,
haven, stringr
Robert Kubinec [aut, cre] (<https://orcid.org/0000-0001-6655-4119>)