c(person("Spyros", "Balafas", email = "s.balafas@rug.nl", role = c("aut", "cre")),person("Wim", "Krijnen", role = "aut"),person("Wendy", "Post", role = "ctb"),person("Ernst", "Wit", role = "aut"))
Nonparametric unfolding item response theory (IRT) model for dichotomous data (see W.H. Van Schuur (1984). Structure in Political Beliefs: A New Model for Stochastic Unfolding with Application to European Party Activists, and W.J.Post (1992). Nonparametric Unfolding Models: A Latent Structure Approach). The package implements MUDFOLD (Multiple UniDimensional unFOLDing), an iterative item selection algorithm that constructs unfolding scales from dichotomous preferential-choice data without explicitly assuming a parametric form of the item response functions. Scale diagnostics from Post(1992) and estimates for the person locations proposed by Johnson(2006) and Van Schuur(1984) are also available. This model can be seen as the unfolding variant of Mokken(1971) scaling method.
gtools, boot, zoo, ggplot2, reshape2, stats, utils
Spyros Balafas [aut, cre],
Wim Krijnen [aut],
Wendy Post [ctb],
Ernst Wit [aut]

boot, glmnet, mgcv, ggplot2, zoo, reshape2, stats, utils,
grDevices, broom, dplyr, graphics

c(person("Spyros", "Balafas", email = "sp.balafas@gmail.com", role = c("aut", "cre")),person("Wim", "Krijnen", role = "aut"),person("Wendy", "Post", role = "ctb"),person("Ernst", "Wit", role = "aut"))

Relative Risk Based on the Ratio of Median Unbiased Estimates
person("Rickey", "Carter", email = "carter.rickey@mayo.edu", role = c("aut", "cre"))
Implements an estimator for relative risk based on the median unbiased estimator. The relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. The details of the method are available in Carter et al (2010) <doi:10.1111/j.1467-9876.2010.00711.x>.

Mixture of Gaussian Bayesian Network Model
A package that models continuous multivariate data via
directional acyclic graphs and provides inference based on the
frequentist approach
mugnet.class.R mugnet.def.R mugnet.learn.R mugnet.predict.R
mugnet.sample.R