Rendering Risk Literacy more Transparent (riskyr)
Risk-related information can be expressed in terms of probabilities or frequencies. By providing a toolbox of methods and metrics, we compute, translate, and represent risk-related information in a variety of ways. By offering different, but complementary perspectives on the interplay between key parameters, ‘riskyr’ renders teaching and training of risk literacy more transparent.

Lookup Tables to Generate Poverty Likelihoods and Rates using the Poverty Probability Index (PPI) (ppitables)
The Poverty Probability Index (PPI) is a poverty measurement tool for organizations and businesses with a mission to serve the poor. The PPI is statistically-sound, yet simple to use: the answers to 10 questions about a household’s characteristics and asset ownership are scored to compute the likelihood that the household is living below the poverty line – or above by only a narrow margin. This package contains country-specific lookup data tables used as reference to determine the poverty likelihood of a household based on their score from the country-specific PPI questionnaire. These lookup tables have been extracted from documentation of the PPI found at <https://www.povertyindex.org> and managed by Innovations for Poverty Action <https://www.poverty-action.org>.

Library Snapshot for Packages and Dependencies in Use by Current Session (librarysnapshot)
Generate a local library copy with relevant packages. All packages currently found within the search path – except base packages – will be copied to the directory provided and can be used later on with the .libPaths() function.

Plotting Two-Dimensional Confidence Regions (conf)
Plots the two-dimensional confidence region for probability distribution (Weibull or inverse Gaussian) parameters corresponding to a user given dataset and level of significance. The crplot() algorithm plots more points in areas of greater curvature to ensure a smooth appearance throughout the confidence region boundary. An alternative heuristic plots a specified number of points at roughly uniform intervals along its boundary. Both heuristics build upon the radial profile log-likelihood ratio technique for plotting two-dimensional confidence regions given by Jaeger (2016) <doi:10.1080/00031305.2016.1182946>.

Anomaly Detection with Normal Probability Functions (amelie)
Implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: <https://…/>, and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) <doi:10.1137/1.9781611972733.3>.

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