tests. Energy- statistics concept based on a generalization of

Title: E-Statistics: Multivariate Inference via the Energy of Data
Description: E-statistics (energy) tests and statistics for multivariate and univariate inference,
including distance correlation, one-sample, two-sample, and multi-sample tests for
comparing multivariate distributions, are implemented. Measuring and testing
multivariate independence based on distance correlation, partial distance correlation,
multivariate goodness-of-fit tests, clustering based on energy distance, testing for
multivariate normality, distance components (disco) for non-parametric analysis of
structured data, and other energy statistics/methods are implemented.

person("Maria", "Rizzo", , "mrizzo@bgsu.edu", c("aut", "cre")),
multivariate goodness-of-fit tests, k-groups and hierarchical clustering based on energy
distance, testing for multivariate normality, distance components (disco) for non-parametric
analysis of structured data, and other energy statistics/methods are implemented.
Imports: Rcpp (>= 0.12.6), stats, boot, gsl

Title: Data Published by the United States Federal Energy Regulatory
Description: Data published by the United States Federal Energy Regulatory Commission including
electric company financial data, natural gas company financial data,
hydropower plant data, liquified natural gas plant data, oil company financial data
natural gas company financial data, and natural gas storage field data.
Packaged: 2016-07-24 14:35:27 UTC; paulgovan

Packaged: 2016-09-23 16:38:38 UTC; paulgovan

Packaged: 2018-12-02 01:49:39 UTC; paulgovan

Description: Compute energy landscapes using a digital elevation model (DEM)
Imports: raster, gdistance, rgdal, sp, Matrix

Imports: raster, gdistance, rgdal, rgeos, sp, Matrix

Description: Compute energy landscapes using a digital elevation model raster and body mass of animals. Vignette available at: <https://emilio-berti.github.io/enerscape.html>.

Title: Robust and Sparse Methods for High Dimensional Linear and
Author: Fatma Sevinc KURNAZ and Irene HOFFMANN and Peter FILZMOSER
Maintainer: Fatma Sevinc Kurnaz <fatmasevinckurnaz@gmail.com>
Description: Fully robust versions of the elastic net estimator are introduced for linear and logistic regression, in particular high dimensional data by Kurnaz, Hoffmann and Filzmoser (2017) <DOI:10.1016/j.chemolab.2017.11.017>. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied.
Imports: ggplot2, glmnet, robustHD, grid, reshape, parallel, cvTools,
Packaged: 2018-01-19 20:21:04 UTC; fatmasevinckurnaz