Authors@R: person("Jean-Baptiste", "Hasse", email = "jb-hasse@hotmail.fr", role = c("aut", "cre"))
Description: The past decade has demonstrated an increased need to better understand risks leading to systemic crises. This framework offers scholars, practitioners and policymakers a useful toolbox to explore such risks in financial systems. Specifically, this framework provides popular econometric and network measures to monitor systemic risk and to measure the consequences of regulatory decisions. These systemic risk measures are based on the frameworks of Adrian and Brunnermeier (2016) <doi:10.1257/aer.20120555> and Billio, Getmansky, Lo and Pelizzon (2012) <doi:10.1016/j.jfineco.2011.12.010>.

Title: Functions for Applying the T^2-Test for Equivalence
Description: Contains functions for applying the T^2-test for equivalence.
The T^2-test for equivalence is a multivariate two-sample equivalence test.
Distance measure of the test is the Mahalanobis distance.
For multivariate normally distributed data the T^2-test for equivalence
The function T2EQ() implements the T^2-test for equivalence
The function T2EQ.dissolution.profiles.hoffelder() implements a variant
of the T^2-test for equivalence according to Hoffelder (2016)
equivalence comparison of highly variable dissolution profiles.

Authors@R: c(person("Wilson", "Rojas-Preciado", role = c("aut", "cre"),
person("Purificación", "Galindo-Villardón", role = c("aut","ctb"),
person("Omar", "Ruiz-Barzola", role = c("aut","ctb"),
Description: Covers k-table control analysis using multivariate control charts for qualitative variables using fundamentals of multiple correspondence analysis and multiple factor analysis. The graphs can be shown in a flat or interactive way, in the same way all the outputs can be shown in an interactive shiny panel.
Imports: shiny, shinydashboardPlus, shinydashboard, shinycssloaders,
dplyr, ca, highcharter, stringr, tables, purrr, tidyr,
Author: Wilson Rojas-Preciado [aut, cre],

Description: Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis.
Imports: Rcpp (>= 1.0.5), Rdpack, Rdimtools, maotai, stats, utils