Maintainer: Helios De Rosario-Martinez <helios.derosario@ibv.upv.es>
Description: Analysis of terms in linear and generalized linear models,
on the basis of multiple comparisons of factor contrasts.
Specially suited for the analysis of interaction terms.

Description: Analysis of terms in linear, generalized and mixed linear
models, on the basis of multiple comparisons of factor
contrasts.  Specially suited for the analysis of interaction

Description: Analysis of terms in linear, generalized and mixed linear models,
on the basis of multiple comparisons of factor contrasts.  Specially suited

Authors@R: c(person("Helios", "De Rosario-Martinez", role=c("aut", "cre"),
Author: Helios De Rosario-Martinez [aut, cre],
Maintainer: Helios De Rosario-Martinez <helios.derosario@gmail.com>

Imports: Matrix, grDevices, methods, utils

Title: Similarity and Distance Quantification Between Probability
Description: Computes 46 optimized distance and similarity measures for comparing probability functions. These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a base framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

Description: Computes 46 optimized distance and similarity measures for comparing probability functions. These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

Packaged: 2018-04-10 14:48:53 UTC; hajk-georg.drost

Description: Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.