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.

Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests

Provides (1) pseudo random generators - general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm () and WELL () generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some generator tests - the gap test, the serial test, the poker test, see, e.g., Gentle (2003) . Take a look at the Distribution task view of types and tests of random number generators. The package can be provided without the 'rngWELL' dependency on demand. Package in Memoriam of Diethelm and Barbara Wuertz.

'WebSocket' Client Library

Provides a 'WebSocket' client interface for R. 'WebSocket' is a protocol for low-overhead real-time communication: < https://en.wikipedia.org/wiki/WebSocket>.

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 Non-Probability Surveys with Population Benchmarks“ .

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.

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.