Title: The generalized hyperbolic distribution
Depends: R (>= 2.3.0), DistributionUtils
Description: This package provides functions for the hyperbolic and
related distributions. Density, distribution and quantile
functions and random number generation are provided for the
hyperbolic distribution, the generalized hyperbolic
distribution, the generalized inverse Gaussian distribution and
the skew-Laplace distribution. Additional functionality is
provided for the hyperbolic distribution, including fitting of

Title: The Generalized Hyperbolic Distribution
Suggests: VarianceGamma, actuar, SkewHyperbolic, MASS
provided for the hyperbolic distribution, normal inverse
Gaussian distribution and generalized inverse Gaussian distribution,
including fitting of these distributions to data.

Depends: R (>= 2.10.0), DistributionUtils, RUnit
Gaussian distribution and generalized inverse Gaussian
distribution, including fitting of these distributions to data.
Linear models with hyperbolic errors may be fitted using

Suggests: VarianceGamma, actuar, SkewHyperbolic, RUnit
Description: Functions for the hyperbolic and related distributions.
Density, distribution and quantile functions and random number generation
are provided for the hyperbolic distribution, the generalized hyperbolic
including fitting of these distributions to data. Linear models with
hyperbolic errors may be fitted using hyperblmFit.

Title: Credible Visualization for Two-Dimensional Projections of Data
Description: Projections from a high-dimensional data space onto a two-dimensional plane are used to detect structures, such as clusters, in multivariate data. The generalized Umatrix is able to visualize errors of these two-dimensional scatter plots by using a 3D topographic map.
Suggests: matrixStats, rgl, ggplot2, grid, mgcv

Suggests: matrixStats, rgl, grid, mgcv, png, ProjectionBasedClustering,

Authors@R: c(person("Michael", "Thrun", email= "m.thrun@gmx.net",role=c("aut","cre","cph")),person("Alfred", "Ultsch",role="ths"))
Description: Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018]. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is based on the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.
Suggests: DataVisualizations, DatabionicSwarm, matrixStats, rgl, grid,
mgcv, png, ProjectionBasedClustering, reshape2, fields