Title: Attribute Sampling Plan with Exact Hypergeometric Probabilities
Description: Implements an algorithm for efficient and exact calculation of hypergeometric
and binomial probabilities using Chebyshev polynomials, while other algorithm use an
approximation when N is large. A useful applications is also considered in this package
for the construction of attribute sampling plans which is an important field of statistical
quality control. The quantile, and the confidence limit for the attribute sampling plan are
also implemented in this package. The hypergeometric distribution can be represented in
terms of Chebyshev polynomials. This representation is particularly useful in the calculation
of exact values of hypergeometric variables.

Title: High-dimensional kernel density estimation and geometry
Description: Estimates the shape and volume of high-dimensional objects and performs set operations: intersection / overlap, union, unique components, and inclusion test. Can measure the n-dimensional ecological hypervolume and perform species distribution modeling.
Depends: Rcpp, rgl, methods, raster, maps
Packaged: 2013-10-31 08:27:12 UTC; benjaminblonder

Packaged: 2013-12-06 06:38:14 UTC; benjaminblonder

Depends: Rcpp, rgl, methods, raster, maps, R (>= 3.0.0)
Packaged: 2013-12-17 12:23:13 UTC; benjaminblonder

Depends: Rcpp, rgl, methods, R (>= 3.0.0)
Packaged: 2014-01-24 15:32:37 UTC; benjaminblonder

Packaged: 2014-01-24 19:45:28 UTC; benjaminblonder

Packaged: 2013-10-26 08:55:16 UTC; benjaminblonder

Description: Estimates the shape and volume of high-dimensional objects and performs set operations: intersection / overlap, union, unique components, inclusion test, and non-convex feature detection. Can measure the n-dimensional ecological hypervolume and perform species distribution modeling.
Packaged: 2014-06-28 20:23:44 UTC; benjaminblonder

Packaged: 2014-07-08 14:41:27 UTC; benjaminblonder

Packaged: 2014-08-24 19:23:34 UTC; benjaminblonder

Title: High-dimensional Kernel Density Estimation and Geometry
Description: Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and negative feature detection. Uses stochastic geometry approach to high-dimensional kernel density estimation. Builds n-dimensional convex hulls (polytopes). Can measure the n-dimensional ecological hypervolume and perform species distribution modeling.
Packaged: 2014-12-19 05:42:26 UTC; benjaminblonder