Efficiently Generates Random Order Statistic Variables
All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution.

Calculates ordinated diet breadth with some plotting functions.

Rune Haubo B Christensen <rhbc@imm.dtu.dk>
This package implements likelihood based models for
ordinal (ordered categorical) data based on cumulative
probabilities in the framework of cumulative link models. This
includes the important proportional odds model but also allows
for general regression structures for location as well as scale
of the latent distribution, i.e. additive as well as
multiplicative structures, structured thresholds (cut-points),
nominal effects and flexible link functions. Further, a range
of estimation procedures and a range of auxiliary functions are
implemented.

This package implements likelihood based models for
ordinal (ordered categorical) data based on cumulative
probabilities in the framework of cumulative link (mixed)
models. This includes the important proportional odds model but
also allows for general regression structures for location as
well as scale of the latent distribution, i.e. additive as well
as multiplicative structures, structured thresholds
(cut-points), nominal effects, flexible link functions and
random effects. Further, a range of estimation procedures and a
range of auxiliary functions are implemented.

This package implements cumulative link (mixed) models
also known as ordered regression models, proportional odds
models, proportional hazards models for grouped survival times
and ordered logit/probit/... models. Estimation is via maximum
likelihood and mixed models are fitted with the Laplace
approximation and adaptive Gauss-Hermite quadrature. Multiple
random effect terms are allowed and they may be nested, crossed
or partially nested/crossed. Restrictions of symmetry and
equidistance can be imposed on the thresholds (cut-points).
Standard model methods are available (summary, anova,
drop-methods, step, confint, predict etc.) in addition to
profile methods and slice methods for visualizing the
likelihood function and checking convergence.