Optimal Policy Learning

Provides functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based on data in order to maximize empirical welfare. Specifically, 'OPL' allows to find "treatment assignment rules" that maximize the overall welfare, defined as the sum of the policy effects estimated over all the policy beneficiaries. Documentation about 'OPL' is provided by several international articles via Athey et al (2021, ), Kitagawa et al (2018, ), Cerulli (2022, ), the paper by Cerulli (2021, ) and the book by Gareth et al (2013, ).

Power and Sample Size for Health Researchers via Shiny

Power and Sample Size for Health Researchers is a Shiny application that brings together a series of functions related to sample size and power calculations for common analysis in the healthcare field. There are functionalities to calculate the power, sample size to estimate or test hypotheses for means and proportions (including test for correlated groups, equivalence, non-inferiority and superiority), association, correlations coefficients, regression coefficients (linear, logistic, gamma, and Cox), linear mixed model, Cronbach's alpha, interobserver agreement, intraclass correlation coefficients, limit of agreement on Bland-Altman plots, area under the curve, sensitivity and specificity incorporating the prevalence of disease. You can also use the online version at < https://hcpa-unidade-bioestatistica.shinyapps.io/PSS_Health/>.

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.

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. See Signorelli (2024) and Signorelli et al. (2021) for details.

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

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

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) .

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.

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) .

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.