Creates Adjacency Matrices for Lineage Searches

Creates and manages a provenance graph corresponding to the provenance created by the 'rdtLite' package, which collects provenance from R scripts. 'rdtLite' is available on CRAN. The provenance format is an extension of the W3C PROV JSON format (< https://www.w3.org/Submission/2013/SUBM-prov-json-20130424/>). The extended JSON provenance format is described in < https://github.com/End-to-end-provenance/ExtendedProvJson>.

National Road Safety Observatory (ONSV) Style for 'ggplot2' Graphics

Helps to create 'ggplot2' charts in the style used by the National Road Safety Observatory (ONSV). The package includes functions to customize 'ggplot2' objects with new theme and colors.

Discovery of Motifs in Spatial-Time Series

Allow to identify motifs in spatial-time series. A motif is a previously unknown subsequence of a (spatial) time series with relevant number of occurrences. For this purpose, the Combined Series Approach (CSA) is used.

Turtle Graphics

An implementation of turtle graphics < http://en.wikipedia.org/wiki/Turtle_graphics>. Turtle graphics comes from Papert's language Logo and has been used to teach concepts of computer programming.

A Time-Travelling Debugger

Uses provenance post-execution to help the user understand and debug their script by providing functions to look at intermediate steps and data values, their forwards and backwards lineage, and to understand the steps leading up to warning and error messages. 'provDebugR' uses provenance produced by 'rdtLite' (available on CRAN), stored in PROV-JSON format.

Joint N-Mixture Models for Site-Associated Species

Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.

Comparing and Visualizing Differences Between Surveys

Easily analyze and visualize differences between samples (e.g., benchmark comparisons, nonresponse comparisons in surveys) on three levels. The comparisons can be univariate, bivariate or multivariate. On univariate level the variables of interest of a survey and a comparison survey (i.e. benchmark) are compared, by calculating one of several difference measures (e.g., relative difference in mean), and an average difference between the surveys. On bivariate level a function can calculate significant differences in correlations for the surveys. And on multivariate levels a function can calculate significant differences in model coefficients between the surveys of comparison. All of those differences can be easily plotted and outputted as a table. For more detailed information on the methods and example use see Rohr, B., Silber, H., & Felderer, B. (2024). Comparing the Accuracy of Univariate, Bivariate, and Multivariate Estimates across Probability and Nonprobability Surveys with Population Benchmarks. Sociological Methodology .

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, ).

Leveraging Experiment Lines to Data Analytics

The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) .

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/>.