Package: HighProbability
Title: HighProbability estimates which alternative hypotheses have frequentist or Bayesian probabilities at least as great as a specified threshold, given a list of p-values.
Version: 1.0
Author: David R. Bickel
Description: HighProbability provides a simple, fast, reliable solution to the multiple testing problem. Given a vector of p-values or achieved significance levels computed using standard frequentist inference, HighProbability determines which ones are low enough that their alternative hypotheses can be considered highly probable. The p-value vector may be determined using existing R functions such as t.test, wilcox.test, cor.test, or sample. HighProbability can be used to detect differential gene expression and to solve other problems involving a large number of hypothesis tests.
Maintainer: David R. Bickel <bickel@prueba.info>
License: Mozilla Public License 1.1 (http://www.mozilla.org/MPL/)

Title: Implementation of Voting Rules Electing the Candidate with
Authors@R: person("Adrien", "Fabre", email = "fabre.adri1@gmail.com", role = c("aut", "cre"))
Description: Computes the scores and ranks candidates according to voting rules electing the highest median grade.
Based on "Tie-breaking the highest median: alternatives to the majority judgment", A. Fabre, Social Choice & Welfare (forthcoming as of 2020). The paper is available here: <https://github.com/bixiou/highest_median/raw/master/Tie-breaking%20Highest%20Median%20-%20Fabre%202019.pdf>.
Functions to plot the voting profiles can be found on github: <https://github.com/bixiou/highest_median/blob/master/packages_functions_data.R>.

Authors@R: c(person("Antonio", "Irpino", role = c("aut", "cre"),
Description: In the framework of Symbolic Data Analysis, a relatively new approach
to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e.,
data described by univariate histograms. The  methods and the basic statistics for
on the L2 Wasserstein metric between distributions, i.e., a Euclidean metric
between quantile functions. The package contains unsupervised classification techniques,
least square regression and tools for histogram-valued data and for histogram time series.