A regression framework for response variables which are continuous
self-rating scales such as the Visual Analog Scale (VAS) used in pain
assessment, or the Linear Analog Self-Assessment (LASA) scales in quality
of life studies. These scales measure subjects' perception of an intangible
quantity, and cannot be handled as ratio variables because of their inherent
non-linearity. We treat them as ordinal variables, measured on a continuous
scale. A function (the g function) connects the scale with an underlying
continuous latent variable. The link function is the inverse of the CDF of the
assumed underlying distribution of the latent variable. A variety of
link functions are currently implemented.

A regression framework for response variables which are continuous
self-rating scales such as the Visual Analog Scale (VAS) used in pain
assessment, or the Linear Analog Self-Assessment (LASA) scales in quality
of life studies. These scales measure subjects' perception of an intangible
quantity, and cannot be handled as ratio variables because of their inherent
non-linearity. We treat them as ordinal variables, measured on a continuous
scale. A function (the g function) connects the scale with an underlying
continuous latent variable. The link function is the inverse of the CDF of the
assumed underlying distribution of the latent variable. A variety of
link functions are currently implemented. Such models are described in Manuguerra et al (2020) <doi:10.18637/jss.v096.i08>.

Ordinal Forests: Prediction and Class Width Inference with
Ordinal Target Variables
Ordinal forests (OF) are a method for ordinal regression with high-dimensional
and low-dimensional data that is able to predict the values of the ordinal target variable
for new observations and at the same time estimate the relative widths of the classes of
the ordinal target variable. Using a (permutation-based) variable importance measure it
is moreover possible to rank the importances of the covariates.
OF will be presented in an upcoming technical report by Hornung et al..
The main functions of the package are: ordfor() (construction of OF), predict.ordfor()
(prediction of the target variable values of new observations), and plot.ordfor()
(visualization of the estimated relative widths of the classes of the ordinal target
variable).