Premal Shah, Benjamin Fitzpatrick, James Fordyce
Iterates through a phylogenetic tree to identify regions
of rate variation using the parametric rate comparison test.
partitions, stats, VGAM, MASS, ape, apTreeshape, geiger, laser

partitions, stats, VGAM, MASS, ape, apTreeshape, geiger,
gtools

Runs classical item analysis for multiple-choice test items and polytomous items (e.g., rating scales). The statistics reported in this package can be found in any measurement textbook such as Crocker and Algina (2006, ISBN:9780495395911).

Approximate probability densities by iterated Laplace
Approximations
The iterLap (iterated Laplace Approximations) algorithm
approximates a general (possibly non-normalized) probability
density on R^p, by repeated Laplace approximations to the
difference between current approximation and true density (on
log scale). The final approximation is a mixture of
multivariate normal distributions and might be used for example
as a proposal distribution for importance sampling (eg in
Bayesian applications). The algorithm can be seen as a
computational generalization of the Laplace approximation
suitable for skew or multimodal densities.

The iterLap (iterated Laplace approximation) algorithm
approximates a general (possibly non-normalized) probability
density on R^p, by repeated Laplace approximations to the
difference between current approximation and true density (on
log scale). The final approximation is a mixture of
multivariate normal distributions and might be used for example
as a proposal distribution for importance sampling (eg in
Bayesian applications).  The algorithm can be seen as a
computational generalization of the Laplace approximation
suitable for skew or multimodal densities.

The iterLap (iterated Laplace approximation) algorithm approximates a
general (possibly non-normalized) probability density on R^p, by repeated
Laplace approximations to the difference between current approximation
and true density (on log scale). The final approximation is a mixture of
multivariate normal distributions and might be used for example as a
proposal distribution for importance sampling (eg in Bayesian applications).
The algorithm can be seen as a computational generalization of the Laplace
approximation suitable for skew or multimodal densities.