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.

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.

Providing new german-wide TapeR Models and functions for their evaluation. Included are the most common tree species in Germany (Norway spruce, Scots pine, European larch, Douglas fir, Silver fir as well as European beech, Common/Sessile oak and Red oak). Many other species are mapped to them so that 36 tree species / groups can be processed. Single trees are defined by species code, one or multiple diameters in arbitrary measuring height and tree height. The functions then provide information on diameters along the stem, bark thickness, height of diameters, volume of the total or parts of the trunk and total and component above-ground biomass. It is also possible to calculate assortments from the taper curves. Uncertainty information is provided for diameter, volume and component biomass estimation.

Download and compile any version of the IANA Time Zone Database (also known as Olson database) and make it current in your R session. Beware: on Windows Cygwin is required!

This package provides a set of exploratory data analysis (EDA) tools for visualizing trends, diagnosing data types for beginner-friendly workflows, and automatically routing to suitable statistical tests or trend exploration models. Includes unified plotting functions for trend lines, grouped boxplots, and comparative scatterplots; automated statistical testing (e.g., t-test, Wilcoxon, ANOVA, Kruskal-Wallis, Tukey, Dunn) with optional effect size calculation; and model-based trend analysis using generalized additive models (GAM) for count data, generalized linear models (GLM) for continuous data, and zero-inflated models (ZIP/ZINB) for count data with potential zero-inflation. Also supports time-window continuity checks, cross-year handling in compare_monthly_cases(), and ARIMA-ready preparation with stationarity diagnostics, ensuring consistent parameter styles for reproducible research and user-friendly workflows.Methods are based on R Core Team (2024) <https://www.R-project.org/>, Wood, S.N.(2017, ISBN:978-1498728331), Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>, Simon Jackman (2024) <https://github.com/atahk/pscl/>, Achim Zeileis, Christian Kleiber, Simon Jackman (2008) <doi:10.18637/jss.v027.i08>.

The R implementation of TIGER. TIGER integrates random forest algorithm into an innovative ensemble learning architecture. Benefiting from this advanced architecture, TIGER is resilient to outliers, free from model tuning and less likely to be affected by specific hyperparameters. TIGER supports targeted and untargeted metabolomics data and is competent to perform both intra- and inter-batch technical variation removal. TIGER can also be used for cross-kit adjustment to ensure data obtained from different analytical assays can be effectively combined and compared. Reference: Han S. et al. (2022) <doi:10.1093/bib/bbab535>.

This package implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. Using marginal structural models, it can estimate intention-to-treat and per-protocol effects in emulated trials using electronic health records. A description and application of the method can be found in Danaei et al (2013) <doi:10.1177/0962280211403603>.

This package provides access to the complete Pali Canon, or Tipitaka, the canonical scripture for Theravadin Buddhists worldwide. Based on the Chattha Sangayana Tipitaka version 4 (Vipassana Research Institute, 1990). Includes word frequency data and tools for Pali string sorting. For a lemmatized critical edition with sutta-level granularity, see the companion package tipitaka.critical'.

Generating Tag and Word Clouds.

This package implements the truncated harmonic mean estimator (THAMES) of the reciprocal marginal likelihood for uni- and multivariate mixture models using posterior samples and unnormalized log posterior values via reciprocal importance sampling. Metodiev, Irons, Perrot-Dockès, Latouche & Raftery (2025) <doi:10.48550/arXiv.2504.21812>.

Convert T Cell Receptor (TCR) gene names between the 10X Genomics, Adaptive Biotechnologies, and ImMunoGeneTics (IMGT) nomenclatures.

This package provides datasets in a format that can be easily consumed by torch dataloaders'. Handles data downloading from multiple sources, caching and pre-processing so users can focus only on their model implementations.

Transfer learning for generalized factor models with support for continuous, count (Poisson), and binary data types. The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1 and IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain.

This package implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) <doi:10.48550/arXiv.2506.19988>. Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses.

Tightens an observational block design into a smaller design with either smaller or fewer blocks while controlling for covariates. The method uses fine balance, optimal subset matching (Rosenbaum, 2012 <doi:10.1198/jcgs.2011.09219>) and two-criteria matching (Zhang et al 2023 <doi:10.1080/01621459.2021.1981337>). The main function is tighten(). The suggested rrelaxiv package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) <doi:10.1007/BF02288322>, (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at <https://github.com/josherrickson/rrelaxiv/>, (iv) is not essential to use the package.

Calculates empirical TL-moments (trimmed L-moments) of arbitrary order and trimming, and converts them to distribution parameters.

Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned through random search.

This package provides a tm Source to create corpora from articles exported from the Dow Jones Factiva content provider as XML or HTML files. It is able to read both text content and meta-data information (including source, date, title, author, subject, geographical coverage, company, industry, and various provider-specific fields).

An inverse probability of censoring weighted (IPCW) targeted maximum likelihood estimator (TMLE) for evaluating a marginal point treatment effect from data where some variables were collected on only a subset of participants using a two-stage design (or marginal mean outcome for a single arm study). A TMLE for conditional parameters defined by a marginal structural model (MSM) is also available.

This package provides a collection of functions for data analysis with two-by-two contingency tables. The package provides tools to compute measures of effect (odds ratio, risk ratio, and risk difference), calculate impact numbers and attributable fractions, and perform hypothesis testing. Statistical analysis methods are oriented towards epidemiological investigation of relationships between exposures and outcomes.

Delta Method implementation to estimate standard errors with known asymptotic properties within the tidyverse workflow. The Delta Method is a statistical tool that approximates an estimatorâ s behaviour using a Taylor Expansion. For a comprehensive explanation, please refer to Chapter 3 of van der Vaart (1998, ISBN: 9780511802256).

Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong. Algorithms proposed by Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multivariate Student's t distribution is also developed. Moreover, the proposed method and computation remain valid for high dimensional and strong dependence scenarios. Empirical results in Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> illustrated the superior performance in terms of various criteria (e.g. mixing and integrated auto-correlation time).

Theme ggplot2', lattice', and base graphics based on a few choices, including foreground color, background color, accent color, and font family. Fonts that aren't available on the system, but are available via download on Google Fonts', can be automatically downloaded, cached, and registered for use with the showtext and ragg packages.

The Gene Expression Omnibus (<https://www.ncbi.nlm.nih.gov/geo/>) and The Cancer Genome Atlas (<https://portal.gdc.cancer.gov/>) are widely used medical public databases. Our platform integrates routine analysis and visualization tools for expression data to provide concise and intuitive data analysis and presentation.