Record Linkage Functions for Linking and Deduplicating Data Sets

Provides functions for linking and deduplicating data sets. Methods based on a stochastic approach are implemented as well as classification algorithms from the machine learning domain. For details, see our paper "The RecordLinkage Package: Detecting Errors in Data" Sariyar M / Borg A (2010) .

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.

Limpa e Simplifica Nomes de Pessoas (Name Cleaner and Simplifier)

Limpa e simplifica nomes de pessoas para auxiliar no pareamento de banco de dados na ausência de chaves únicas não ambíguas. Detecta e corrige erros tipográficos mais comuns, simplifica opcionalmente termos sujeitos eventualmente a omissão em cadastros, e simplifica foneticamente suas palavras, aplicando variação própria do algoritmo metaphoneBR. (Cleans and simplifies person names to assist in database matching when unambiguous unique keys are unavailable. Detects and corrects common typos, optionally simplifies terms prone to omission in records, and applies phonetic simplification using a custom variation of the metaphoneBR algorithm.) Mation (2025) .

Custom 'MetaphoneBR' Phonetic Encoding for Brazilian Names

Simplifies Brazilian names phonetically using a custom 'metaphoneBR' algorithm that preserves ending vowels. Useful for name matching processing preserving gender information carried generally by ending vowels in Portuguese. Mation (2025) .

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 .

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.