This is a companion package for the text2sdg package. It contains the trained ensemble models needed by the detect_sdg function from the text2sdg package. See Wulff, Meier and Mata (2023) <arXiv:2301.11353> and Meier, Wulff and Mata (2021) <arXiv:2110.05856> for reference.

An easy way to examine archaeological count data. This package provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). It allows to easily visualize count data and statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams, etc.

This package provides functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in tidybayes <https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.

Visualize your Tidyverse data analysis pipelines via the Tidy Data Tutor'(<https://tidydatatutor.com/>) web application.

Pure R implementation of Apache Thrift. This library doesn't require any code generation. To learn more about Thrift go to <https://thrift.apache.org>.

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.

This package provides a pipeline of tools for analysing circadian time-series data using functional data analysis (FDA). The package supports smoothing of rhythmic time series, functional principle component analysis (FPCA), and extraction of group-level traits from functional representations. Analyses can incorporate multiple curve derivatives and optional temporal segmentation, enabling comparative analysis of circadian dynamics across experimental groups and time windows.

Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series. Zekai, S. (2011). <doi:10.1061/(ASCE)HE.1943-5584.0000556>. Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp.

This package provides a robust implementation of Topolow algorithm. It embeds objects into a low-dimensional Euclidean space from a matrix of pairwise dissimilarities, even when the data do not satisfy metric or Euclidean axioms. The package is particularly well-suited for sparse, incomplete, and censored (thresholded) datasets such as antigenic relationships. The core is a physics-inspired, gradient-free optimization framework that models objects as particles in a physical system, where observed dissimilarities define spring rest lengths and unobserved pairs exert repulsive forces. The package also provides functions specific to antigenic mapping to transform cross-reactivity and binding affinity measurements into accurate spatial representations in a phenotype space. Key features include: * Robust Embedding from Sparse Data: Effectively creates complete and consistent maps (in optimal dimensions) even with high proportions of missing data (e.g., >95%). * Physics-Inspired Optimization: Models objects (e.g., antigens, landmarks) as particles connected by springs (for measured dissimilarities) and subject to repulsive forces (for missing dissimilarities), and simulates the physical system using laws of mechanics, reducing the need for complex gradient computations. * Automatic Dimensionality Detection: Employs a likelihood-based approach to determine the optimal number of dimensions for the embedding/map, avoiding distortions common in methods with fixed low dimensions. * Noise and Bias Reduction: Naturally mitigates experimental noise and bias through its network-based, error-dampening mechanism. * Antigenic Velocity Calculation (for antigenic data): Introduces and quantifies "antigenic velocity," a vector that describes the rate and direction of antigenic drift for each pathogen isolate. This can help identify cluster transitions and potential lineage replacements. * Broad Applicability: Analyzes data from various objects that their dissimilarity may be of interest, ranging from complex biological measurements such as continuous and relational phenotypes, antibody-antigen interactions, and protein folding to abstract concepts, such as customer perception of different brands. Methods are described in the context of bioinformatics applications in Arhami and Rohani (2025a) <doi:10.1093/bioinformatics/btaf372>, and mathematical proofs and Euclidean embedding details are in Arhami and Rohani (2025b) <doi:10.48550/arXiv.2508.01733>.

This package provides a tm Source to create corpora from articles exported from the Europresse content provider as HTML files. It is able to read both text content and meta-data information (including source, date, title, author and pages).

Several functions to allow comparisons of data across different geographies, in particular for Canadian census data from different censuses.

This package provides user-friendly tools for creating and customizing clinical trial reports. By leveraging the teal framework, this package provides teal modules to easily create an interactive panel that allows for seamless adjustments to data presentation, thereby streamlining the creation of detailed and accurate reports.

Calculation of string distance following the tidy data principles. Built on top of the stringdist package.

Helper functions for creating, editing, and testing tutorials created with the learnr package. Provides a simple method for allowing students to download their answers to tutorial questions. For examples of its use, see the r4ds.tutorials package.

This package creates interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. treeheatr utilizes the customizable ggparty package for drawing decision trees.

Provide functions to estimate the coefficients in high-dimensional linear regressions via a tuning-free and robust approach. The method was published in Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "A Tuning-free Robust and Efficient Approach to High-dimensional Regression", Journal of the American Statistical Association, 115:532, 1700-1714(JASAâ s discussion paper), <doi:10.1080/01621459.2020.1840989>. See also Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "Rejoinder to â A tuning-free robust and efficient approach to high-dimensional regression". Journal of the American Statistical Association, 115, 1726-1729, <doi:10.1080/01621459.2020.1843865>; Peng, B. and Wang, L. (2015), "An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression", Journal of Computational and Graphical Statistics, 24:3, 676-694, <doi:10.1080/10618600.2014.913516>; Clémençon, S., Colin, I., and Bellet, A. (2016), "Scaling-up empirical risk minimization: optimization of incomplete u-statistics", The Journal of Machine Learning Research, 17(1):2682â 2717; Fan, J. and Li, R. (2001), "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties", Journal of the American Statistical Association, 96:456, 1348-1360, <doi:10.1198/016214501753382273>.

This package implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).

Uniform random samples from simple manifolds, sometimes with noise, are commonly used to test topological data analytic (TDA) tools. This package includes samplers powered by two techniques: analytic volume-preserving parameterizations, as employed by Arvo (1995) <doi:10.1145/218380.218500>, and rejection sampling, as employed by Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.

General framework to organize data, methods, and results used in reproducible scientific analyses. A TAF analysis consists of four scripts (data.R, model.R, output.R, report.R) that are run sequentially. Each script starts by reading files from a previous step and ends with writing out files for the next step. Convenience functions are provided to version control the required data and software, run analyses, clean residues from previous runs, manage files, manipulate tables, and produce figures. With a focus on stability and reproducible analyses, the TAF package comes with no dependencies. TAF forms a base layer for the icesTAF package and other scientific applications.

Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.

Social Relation Model (SRM) analyses for single or multiple round-robin groups are performed. These analyses are either based on one manifest variable, one latent construct measured by two manifest variables, two manifest variables and their bivariate relations, or two latent constructs each measured by two manifest variables. Within-group t-tests for variance components and covariances are provided for single groups. For multiple groups two types of significance tests are provided: between-groups t-tests (as in SOREMO) and enhanced standard errors based on Lashley and Bond (1997) <DOI:10.1037/1082-989X.2.3.278>. Handling for missing values is provided.

This package provides a convenient way to log scalars, images, audio, and histograms in the tfevent record file format. Logged data can be visualized on the fly using TensorBoard', a web based tool that focuses on visualizing the training progress of machine learning models.

Testing for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha & Song 2022) <doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension test quantifies the statistical evidence for trajectory presence. The subset specificity measure summarizes pattern heterogeneity using the minimum subtree cover. There is no user tunable parameters for either method. Examples are included to illustrate how to use the methods on single-cell data for studying gene and pathway expression dynamics and pathway expression specificity.

This package provides convenience functions for common data modification and analysis tasks in communication research. This includes functions for univariate and bivariate data analysis, index generation and reliability computation, and intercoder reliability tests. All functions follow the style and syntax of the tidyverse, and are construed to perform their computations on multiple variables at once. Functions for univariate and bivariate data analysis comprise summary statistics for continuous and categorical variables, as well as several tests of bivariate association including effect sizes. Functions for data modification comprise index generation and automated reliability analysis of index variables. Functions for intercoder reliability comprise tests of several intercoder reliability estimates, including simple and mean pairwise percent agreement, Krippendorff's Alpha (Krippendorff 2004, ISBN: 9780761915454), and various Kappa coefficients (Brennan & Prediger 1981 <doi: 10.1177/001316448104100307>; Cohen 1960 <doi: 10.1177/001316446002000104>; Fleiss 1971 <doi: 10.1037/h0031619>).