Title: A Rapid Digital Image Analysis for Leaf Area
Description: 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.
SystemRequirements: ImageJ 1.48 (see http://imagej.nih.gov/ij/)

Title: Rapid Digital Image Analysis of Leaf Area

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

Description: 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
SystemRequirements: ImageJ (>=1.48), ij.jar (see

Description: An interface for the image processing program 'ImageJ', which
function to write an 'ImageJ' macro which is optimized for a leaf area analysis by

Title: Functions for Learning Bayesian Inference
Description: 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.
Packaged: Thu May  3 08:11:23 2007; Jim Albert

Packaged: Thu Apr 17 11:15:57 2008; Jim Albert

Packaged: Sun Jan  4 19:12:38 2009; Jim Albert

Description: 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

Description: 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.
Packaged: 2018-03-18 17:46:55 UTC; jamesalbert