An efficient algorithm for data twinning. This work is supported by U.S. National Science Foundation grants DMREF-1921873 and CMMI-1921646.

This package provides a tool to obtain tumor growth rates from clinical trial patient data. Output includes individual and summary data for tumor growth rate estimates as well as optional plots of the observed and predicted tumor quantity over time.

This package contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: tPCA', tFOBI', tJADE', k-tJADE', tgFOBI', tgJADE', tSOBI', tNSS.SD', tNSS.JD', tNSS.TD.JD', tPP and tTUCKER'.

Wrapper for using tapkee command line utility, it allows to run it from inside R and catch the results for further analysis and plotting. Tapkee is a program for fast dimension reduction, see package?tapkee and <http://tapkee.lisitsyn.me/> for installation and other details.

Finding the best values for user-specified arguments of a prediction algorithm can be difficult, particularly if there is an interaction between argument levels. This package automates the testing of any user-defined prediction algorithm over an arbitrary number of arguments. It includes functions for testing the algorithm over the given arguments with respect to an arbitrary number of user-defined diagnostics, visualising the results of these tests, and finding the optimal argument combinations with respect to each diagnostic.

This package provides tools for decomposing differences in rate metrics between two groups into contributions from individual subgroups and visualizing them as a "Theseus Plot". Inspired by the story of the Ship of Theseus, the method replaces subgroup data from one group with that of another step by step, recalculating the overall metric at each stage to quantify subgroup contributions. A Theseus Plot combines the stepwise progression of a waterfall plot with the comparative bars of a bar chart, offering an intuitive way to understand subgroup-level effects.

An extension of ExPosition for two table analyses, specifically, discriminant analyses.

This package provides a version of the Titanic survival data tailored for people analytics demonstrations and practice. While another package, titanic', reproduces the Kaggle competition files with minimal preprocessing, tidytitanic combines the train and test datasets into the single dataset, passengers', for exploration and summary across all passengers. It also extracts personal identifiersâ such as first names, last names, and titles from the raw name field, enabling demographic analysis. The passengers data does not cover the crew, but this package also provides the more bare-bones, crew-containing datasets tidy_titanic and flat_titanic based on the Titanic data set from datasets for further exploration. This human-centered data package is designed to support exploratory data analysis, feature engineering, and pedagogical use cases.

Approximations of global p-values when testing hypothesis in presence of non-identifiable nuisance parameters. The method relies on the Euler characteristic heuristic and the expected Euler characteristic is efficiently computed by in Algeri and van Dyk (2018) <arXiv:1803.03858>.

The United Nations Sustainable Development Goals (SDGs) have become an important guideline for organisations to monitor and plan their contributions to social, economic, and environmental transformations. The text2sdg package is an open-source analysis package that identifies SDGs in text using scientifically developed query systems, opening up the opportunity to monitor any type of text-based data, such as scientific output or corporate publications. For more information see Meier, Mata & Wulff (2025) <doi:10.32614/RJ-2024-005> and Wulff, Meier & Mata (2024) <doi:10.1007/s11625-024-01516-3>.

Optimizers for torch deep learning library. These functions include recent results published in the literature and are not part of the optimizers offered in torch'. Prospective users should test these optimizers with their data, since performance depends on the specific problem being solved. The packages includes the following optimizers: (a) adabelief by Zhuang et al (2020), <arXiv:2010.07468>; (b) adabound by Luo et al.(2019), <arXiv:1902.09843>; (c) adahessian by Yao et al.(2021) <arXiv:2006.00719>; (d) adamw by Loshchilov & Hutter (2019), <arXiv:1711.05101>; (e) madgrad by Defazio and Jelassi (2021), <arXiv:2101.11075>; (f) nadam by Dozat (2019), <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) qhadam by Ma and Yarats(2019), <arXiv:1810.06801>; (h) radam by Liu et al. (2019), <arXiv:1908.03265>; (i) swats by Shekar and Sochee (2018), <arXiv:1712.07628>; (j) yogi by Zaheer et al.(2019), <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.

Test functions are often used to test computer code. They are used in optimization to test algorithms and in metamodeling to evaluate model predictions. This package provides test functions that can be used for any purpose.

You only need to type why pie charts are bad on Google to find thousands of articles full of (valid) reasons why other types of charts should be preferred over this one. Therefore, because of the little use due to the reasons already mentioned, making pie charts (and related) in R is not straightforward, so other functions are needed to simplify things. In this R package there are useful functions to make tasty pie charts immediately by exploiting the many cool templates provided.

This package creates a local Lightning Memory-Mapped Database ('LMDB') of many commonly used taxonomic authorities and provides functions that can quickly query this data. Supported taxonomic authorities include the Integrated Taxonomic Information System ('ITIS'), National Center for Biotechnology Information ('NCBI'), Global Biodiversity Information Facility ('GBIF'), Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). Name and identifier resolution using LMDB can be hundreds of times faster than either relational databases or internet-based queries. Precise data provenance information for data derived from naming providers is also included.

This package provides functions to get personal Google Scholar profile data from web API and show it in table or figure format.

Calculates the number of true positives and false positives from a dataset formatted for Jackknife alternative free-response receiver operating characteristic which is used for statistical analysis which is explained in the book Chakraborty DP (2017), "Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples", Taylor-Francis <https://www.crcpress.com/9781482214840>.

Tidy standardized mean differences ('SMDs'). tidysmd uses the smd package to calculate standardized mean differences for variables in a data frame, returning the results in a tidy format.

Themes for ggplot2 are a convenient way to style plots. The hrbrthemes package contains a particularly nice one, but brings along a significant tail of dependencies. So this (currently experimental) package brings along just the theme_ipsum_rc theme using the Roboto Condensed font. Should the font not be installed on your system, see the help in the package hrbrthemes on how to install Roboto Condensed'. Note that hrbrthemes is now archived at CRAN.

Treatment and visualization of membrane (selective) transport data. Transport profiles involving up to three species are produced as publication-ready plots and several membrane performance parameters (e.g. separation factors as defined in Koros et al. (1996) <doi:10.1351/pac199668071479> and non-linear regression parameters for the equations described in Rodriguez de San Miguel et al. (2014) <doi:10.1016/j.jhazmat.2014.03.052>) can be obtained. Many widely used experimental setups (e.g. membrane physical aging) can be easily studied through the package's graphical representations.

Implementation of unconditional Bernoulli Scan Statistic developed by Kulldorff et al. (2003) <doi:10.1111/1541-0420.00039> for hierarchical tree structures. Tree-based Scan Statistics are an exploratory method to identify event clusters across the space of a hierarchical tree.

Analyze telemetry datasets generalized to allow any technology. The filtering steps check for false positives caused by reflected transmissions from surfaces and false pings from other noise generating equipment. The filters are based on JSATS filtering algorithms found in package filteRjsats <https://CRAN.R-project.org/package=filteRjsats> but have been generalized to allow the user to define many of the filtering variables. Additionally, this package contains scripts used to help identify an optimal maximum blanking period as defined in Capello et al (2015) <doi:10.1371/journal.pone.0134002>. The functions were written according to their manuscript description, but have not been reviewed by the authors for accuracy. It is included here as is, without warranty.

The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) <DOI:10.1016/j.ijforecast.2010.04.009>.

Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis.

This package provides methods for generating modelled parametric Tropical Cyclone (TC) spatial hazard fields and time series output at point locations from TC tracks. R's compatibility to simply use fast cpp code via the Rcpp package and the wide range spatial analysis tools via the terra package makes it an attractive open source environment to study TCs'. This package estimates TC vortex wind and pressure fields using parametric equations originally coded up in python by TCRM <https://github.com/GeoscienceAustralia/tcrm> and then coded up in Cuda cpp by TCwindgen <https://github.com/CyprienBosserelle/TCwindgen>.