We consider the problem of estimating two isotonic regression curves g1* and g2* under the constraint that they are ordered, i.e. g1* <= g2*. Given two sets of n data points y_1, ..., y_n and z_1, ..., z_n that are observed at (the same) deterministic design points x_1, ..., x_n, the estimates are obtained by minimizing the Least Squares criterion L(a, b) = sum_{i=1}^n (y_i - a_i)^2 w1(x_i) + sum_{i=1}^n (z_i - b_i)^2 w2(x_i) over the class of pairs of vectors (a, b) such that a and b are isotonic and a_i <= b_i for all i = 1, ..., n. We offer two different approaches to compute the estimates: a projected subgradient algorithm where the projection is calculated using a PAVA as well as Dykstra's cyclical projection algorithm.

Concurrent generation of ordinal and normal data with given
correlation matrix and marginal distributions
The package implements a procedure for generating samples from a mixed distribution of ordinal and normal random variables with pre-specified correlation matrix and marginal distributions.

Concurrent Generation of Ordinal and Normal Data with Given
Correlation Matrix and Marginal Distributions
Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with pre-specified correlation matrix and marginal distributions.

Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions.
The details of the method are explained in Demirtas et al. (2015) <DOI:10.1080/10543406.2014.920868>.

Biplot representations of ordinal variables
Analysis of a matrix of polytomous items using Ordinal Logistic Biplots (OLB)
The OLB procedure extends the binary logistic biplot to ordinal (polytomous) data.
The  individuals are represented as points on a plane and the variables are represented
as lines rather than vectors as in a classical or binary biplot, specifying the points
for each of the categories of the variable.
The set of prediction regions is established by stripes perpendicular to the line
between the category points, in such a way that the prediction for each individual is given
by its projection into the line of the variable.