Title: A graphical user interface for calculating power and sample size
Description: HierO is a graphical user interface (GUI) tool for calculating optimal statistical power and sample size for hierarchical data structure. HierO constructs a user defined sample size optimization problem to GAMS (General Algebraic Modeling System)form and uses Rneos package to send the problem to NEOS server for solving.
Imports: XMLRPC, RCurl, bitops, XML, tcltk, tcltk2

Imports: RCurl, bitops, XML, tcltk, tcltk2

Title: Hierarchical Clustering-Based Portfolio Allocation Strategies
Description: Machine learning portfolio allocation strategies based on hierarchical clustering methods.
Hierarchical risk parity (De Prado, 2016) <DOI: 10.3905/jpm.2016.42.4.059> and
Hierarchical clustering-based asset allocation (Raffinot, 2017)
Packaged: 2021-11-06 21:12:19 UTC; ctruciosm

Packaged: 2021-11-09 15:37:00 UTC; ctruciosm

Title: Outlier Detection Algorithms for High-Dimensional Data
Imports: foreach, DMwR, plyr, proxy, FNN, ggplot2
Description: Three high-dimensional outlier detection algorithms and a outlier unification scheme are implemented in this package. The angle-based outlier detection (ABOD) algorithm is based on the work of Kriegel, Schubert, and Zimek [2008]. The subspace outlier detection (SOD) algorithm is based on the work of Kriegel, Kroger, Schubert, and Zimek [2009]. The feature bagging-based outlier detection (FBOD) algorithm is based on the work of Lazarevic and Kumar [2005]. The outlier unification scheme is based on the work of Kriegel, Kroger, Schubert, and Zimek [2011].

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: 1.0-2
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/)
Packaged: Wed May  5 11:22:18 2004; hornik