Generate LaTeX tables directly from R. It builds LaTeX tables in blocks in the spirit of ggplot2 using the + and / operators for concatenation in the vertical and horizontal dimensions, respectively. It exports tables in the LaTeX tabular environment using .tex code. It can compile .tex code to PDF automatically.

The trigger strategy is a general framework for a multistage statistical design with multiple hypotheses, allowing an adaptive selection of interim analyses. The selection of interim stages can be associated with some prespecified endpoints which serve as the trigger. This selection allows us to refine the critical boundaries in hypotheses testing procedures, and potentially increase the statistical power. This package includes several trial designs using the trigger strategy. See Gou, J. (2023), "Trigger strategy in repeated tests on multiple hypotheses", Statistics in Biopharmaceutical Research, 15(1), 133-140, and Gou, J. (2022), "Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints", Biometrical Journal, 64(2), 301-311.

Matching terminal restriction fragment length polymorphism ('TRFLP') profiles between unknown samples and a database of known samples. TRAMPR facilitates analysis of many unknown profiles at once, and provides tools for working directly with electrophoresis output through to generating summaries suitable for community analyses with R's rich set of statistical functions. TRAMPR also resolves the issues of multiple TRFLP profiles within a species, and shared TRFLP profiles across species.

Trusted Timestamps (tts) are created by incorporating a hash of a file or dataset into a transaction on the decentralized blockchain (Stellar network). The package makes use of a free service provided by <https://stellarapi.io>.

Extension of funHDDC Schmutz et al. (2018) <doi:10.1007/s00180-020-00958-4> for cases including outliers by fitting t-distributions for robust groups. TFunHDDC can cluster univariate or multivariate data produced by the fda package for data using a b-splines or Fourier basis.

Computes correlation matrices as tidy data frames and creates publication-ready correlograms with ggplot2'. The package is designed for teaching and exploratory analysis workflows where users want one consistent interface for selecting numeric variables, calculating pairwise correlations, optionally estimating p-values, reordering variables, and drawing tile, point, or mixed correlograms.

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 a tidy set of functions for summarising data, including descriptive statistics, frequency tables with normality testing, and group-wise significance testing. Designed for fast, readable, and easy exploration of both numeric and categorical data.

This package provides a hypothesis test and variable selection algorithm for use in time-varying, concurrent regression models. The hypothesis test function is also accompanied by a plotting function which will show the estimated beta(s) and confidence band(s) from the hypothesis test. The hypothesis test function helps the user identify significant covariates within the scope of a time-varying concurrent model. The plots will show the amount of area that falls outside the confidence band(s) which is used for the test statistic within the hypothesis test.

Create interactive tables, calendars, charts and markdown WYSIWYG editor with TOAST UI <https://ui.toast.com/> libraries to integrate in shiny applications or rmarkdown HTML documents.

Create a time-varying dataset using features, exposure, and look back specifications.

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

This package provides a clinically meaningful measures of treatment effects for right-censored data are provided, based on the concept of Kendall's tau, along with the corresponding inference procedures. Two plots of tau processes, with the option to account for the cure fraction or not, are available. The plots of tau processes serve as useful graphical tools for monitoring the relative performances over time.

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>.

This package provides a framework for high-dimensional mediation analysis using transfer learning. The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) <doi:10.1093/bib/bbaf460>.

Streamline the processing of Telraam data, sourced from open data mobility sensors. These tools range from data retrieval (without the need for API knowledge) to data visualization, including data preprocessing.

This package provides functions to access the database of 217 data-frames with aggregate study-level characteristics (that may act as effect modifiers) extracted from published systematic reviews with network meta-analysis. The database shall only be used for developing and appraising the methodology to assess the transitivity assumption quantitatively.

ARIMA-model-based decomposition of quarterly and monthly time series data. The methodology is developed and described, among others, in Burman (1980) <DOI:10.2307/2982132> and Hillmer and Tiao (1982) <DOI:10.2307/2287770>.

This package provides tools to deploy TensorFlow <https://www.tensorflow.org/> models across multiple services. Currently, it provides a local server for testing cloudml compatible services.

There are some experimental scenarios where each experimental unit receives a sequence of treatments across multiple periods, and treatment effects persist beyond the period of application. It focuses on the construction and calculation of the parametric value of the residual effect designs balanced for carryover effects, also referred to as crossover designs, change-over designs, or repeated measurements designs (Aggarwal and Jha, 2010<doi:10.1080/15598608.2010.10412013>). The primary objective of the package is to generate a new class of Balanced Ternary Residual Effect Designs (BTREDs), balanced for carryover effects tailored explicitly for situations where the number of periods is less than or equal to the number of treatments. In addition, the package provides four new classes of Partially Balanced Ternary Residual Effect Designs (PBTREDs), constructed using incomplete block designs, initial sequences, and rectangular association scheme. In addition, one extra function is included to help study the parametric properties of a given residual effect design.

Set of functions designed to help in the analysis of TDP sensors. Features includes dates and time conversion, weather data interpolation, daily maximum of tension analysis and calculations required to convert sap flow density data to sap flow rates at the tree and plot scale (For more information see : Granier (1985) <DOI:10.1051/forest:19850204> & Granier (1987) <DOI:10.1093/treephys/3.4.309>).

This package creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.

Most estimators implemented by the video game industry cannot obtain reliable initial estimates nor guarantee comparability between distant estimates. TrueSkill Through Time solves all these problems by modeling the entire history of activities using a single Bayesian network allowing the information to propagate correctly throughout the system. This algorithm requires only a few iterations to converge, allowing millions of observations to be analyzed using any low-end computer. Landfried G, Mocskos E (2025). "TrueSkill Through Time: Reliable Initial Skill Estimates and Historical Comparability with Julia, Python, and R." <doi:10.18637/jss.v112.i06>. The core ideas implemented in this project were developed by Dangauthier P, Herbrich R, Minka T, Graepel T (2007). "Trueskill through time: Revisiting the history of chess.".

Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. Further information regarding the use of the package, along with detailed examples, can be found in McGonigle, Killick and Nunes (2025) <doi:10.18637/jss.v115.i10>. New users will likely want to start with the TLSW function.