Summarizes Provenance Related to Inputs and Outputs of a Script or Console Commands

Reads the provenance collected by the 'rdtLite' or 'rdt' packages, or other tools providing compatible PROV JSON output, created by the execution of a script or a console session, and provides a human-readable summary identifying the input and output files, the scripts used (if any), errors and warnings produced, and the environment in which it was executed. It can also optionally package all the files into a zip file. The exact format of the PROV JSON file created by 'rdtLite' and 'rdt' is described in < https://github.com/End-to-end-provenance/ExtendedProvJson>. More information about 'rdtLite' and associated tools is available at < https://github.com/End-to-end-provenance/> and Lerner, Boose, and Perez (2018), Using Introspection to Collect Provenance in R, Informatics, .

Uses Provenance to Trace File Lineage for One or more R Scripts

Uses provenance collected by 'rdtLite' package or comparable tool to display information about input files, output files, and exchanged files for a single R script or a series of R scripts.

National Road Safety Observatory (ONSV) Styles for 'gt' Tables

Wrapper functions for customizing HTML tables from the 'gt' package to the ONSV style.

Penalized Regression Calibration (PRC) for the Dynamic Prediction of Survival

Computes penalized regression calibration (PRC), a statistical method for the dynamic prediction of survival when many longitudinal predictors are available. PRC is described in Signorelli (2024) and in Signorelli et al. (2021) .

Download Official Spatial Data Sets of Brazil

Easy access to official spatial data sets of Brazil as 'sf' objects in R. The package includes a wide range of geospatial data available at various geographic scales and for various years with harmonized attributes, projection and fixed topology.

Correspondence Analysis with Geometric Frequency Interpretation

Performs Correspondence Analysis on the given dataframe and plots the results in a scatterplot that emphasizes the geometric interpretation aspect of the analysis, following Borg-Groenen (2005) and Yelland (2010). It is particularly useful for highlighting the relationships between a selected row (or column) category and the column (or row) categories. See Borg-Groenen (2005, ISBN:978-0-387-28981-6); Yelland (2010) .

Dose Rate Modelling of Carbonate-Rich Samples

Translation of the 'MATLAB' program 'Carb' (Nathan and Mauz 2008 ; Mauz and Hoffmann 2014) for dose rate modelling for carbonate-rich samples in the context of trapped charged dating (e.g., luminescence dating) applications.

Plotting Conversation Data

Visualisation, analysis and quality control of conversational data. Rapid and visual insights into the nature, timing and quality of time-aligned annotations in conversational corpora. For more details, see Dingemanse et al., (2022) .

Discrimination Mitigation for Machine Learning Models

Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package is described by Kamiran, Karim and Zhang (2012) < https://ieeexplore.ieee.org/document/6413831/>.

Robust Nonparametric Two-Sample Tests for Location/Scale

Implementations of several robust nonparametric two-sample tests for location or scale differences. The test statistics are based on robust location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators as described in Fried & Dehling (2011) . The p-values can be computed via the permutation principle, the randomization principle, or by using the asymptotic distributions of the test statistics under the null hypothesis, which ensures (approximate) distribution independence of the test decision. To test for a difference in scale, we apply the tests for location difference to transformed observations; see Fried (2012) . Random noise on a small range can be added to the original observations in order to hold the significance level on data from discrete distributions. The location tests assume homoscedasticity and the scale tests require the location parameters to be zero.