Set up, run and explore the outputs of the Length-based Multi-species model (LeMans; Hall et al. 2006 <doi:10.1139/f06-039>), focused on the marine environment.

Fits, plots, and summarizes leaf angle distributions.
A leaf angle distribution is a special distribution that
is defined between 0 and 90 degrees, and a number of
distributions are used to characterize the leaf angle
distribution in real plant canopies. This package includes
methods to fit distributions to data, visualize the fit, and
compare fits of nine different distributions.

Analysis and Visualization of Plant Leaf Angle Distributions
A leaf angle distribution is a special distribution that is defined between 0 and 90 degrees, and a number of distributions are used to characterize the leaf angle distribution in real plant canopies. This package includes methods to fit distributions to data, visualize the fit, and compare fits of nine different distributions.

A Rapid Digital Image Analysis for Leaf Area
An interface for the image processing program ImageJ, which allows a rapid digital image analysis for particle sizes. This package includes function to write an ImageJ macro which is optimized for a leaf area analysis by default.

Rapid Digital Image Analysis of Leaf Area

ImageJ (>=1.48) (see http://imagej.nih.gov/ij/),
Java

An interface for the image processing program ImageJ, which
allows a rapid digital image analysis for particle sizes. This package includes
function to write an ImageJ macro which is optimized for a leaf area analysis by
default.
ImageJ (>=1.48), ij.jar (see
http://imagej.nih.gov/ij/), Java (>=1.6.0)

An interface for the image processing program 'ImageJ', which
allows a rapid digital image analysis for particle sizes. This package includes
function to write an 'ImageJ' macro which is optimized for a leaf area analysis by
default.

Functions for Learning Bayesian Inference
LearnBayes contains a collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.