Estimation of causal odds ratio and power calculation given trends in exposure prevalence and outcome frequencies of stratified data.

This package provides a modular package for simulating phylogenetic trees and species traits jointly. Trees can be simulated using modular birth-death parameters (e.g. changing starting parameters or algorithm rules). Traits can be simulated in any way designed by the user. The growth of the tree and the traits can influence each other through modifiers objects providing rules for affecting each other. Finally, events can be created to modify both the tree and the traits under specific conditions ( Guillerme, 2024 <DOI:10.1111/2041-210X.14306>).

Collaborative writing and editing of R Markdown (or Sweave) documents. The local .Rmd (or .Rnw) is uploaded as a plain-text file to Google Drive. By taking advantage of the easily readable Markdown (or LaTeX) syntax and the well-known online interface offered by Google Docs, collaborators can easily contribute to the writing and editing process. After integrating all authorsâ contributions, the final document can be downloaded and rendered locally.

This package provides functions and example files to calculate the tRNA adaptation index, a measure of the level of co-adaptation between the set of tRNA genes and the codon usage bias of protein-coding genes in a given genome. The methodology is described in dos Reis, Wernisch and Savva (2003) <doi:10.1093/nar/gkg897>, and dos Reis, Savva and Wernisch (2004) <doi:10.1093/nar/gkh834>.

When using the R package exams to write mathematics questions in Sweave files, the output of a lot of R functions need to be adjusted for display in mathematical formulas. Specifically, the functions were accumulated when writing questions for the topics of the mathematics courses College Algebra, Precalculus, Calculus, Differential Equations, Introduction to Probability, and Linear Algebra. The output of the developed functions can be used in Sweave files.

This analytical framework is based on an analysis of the shape of the trait abundance distributions to better understand community assembly processes, and predict community dynamics under environmental changes. This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve).

This package provides a simple Natural Language Processing (NLP) toolkit focused on search-centric workflows with minimal dependencies. The package offers key features for web scraping, text processing, corpus search, and text embedding generation via the HuggingFace API <https://huggingface.co/docs/api-inference/index>.

The goal of tidyheatmaps is to simplify the generation of publication-ready heatmaps from tidy data. By offering an interface to the powerful pheatmap package, it allows for the effortless creation of intricate heatmaps with minimal code.

Framework to run Monte Carlo simulations over a parameter grid. Allows to parallelize the simulations. Generates plots and LaTeX tables summarizing the results from the simulation.

This package provides tools to help developers and producers manipulate R objects and outputs. It includes tools for displaying results and objects, and for formatting them in the correct format.

Data frames with time information are subset and flagged with period information. Data frames with times are dealt as timeDF objects and periods are represented as periodDF objects.

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.

Here we provide tools for the computation and factorization of high-dimensional tensor products that are formed by smaller matrices. The methods are based on properties of Kronecker products (Searle 1982, p. 265, ISBN-10: 0470009616). We evaluated this methodology by benchmark testing and illustrated its use in Gaussian Linear Models ('Lopez-Cruz et al., 2024') <doi:10.1093/g3journal/jkae001>.

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.

Interface to TensorFlow Datasets, a high-level library for building complex input pipelines from simple, re-usable pieces. See <https://www.tensorflow.org/guide> for additional details.

Utilities for handling character vectors that store human-readable text (either plain or with markup, such as HTML or LaTeX). The package provides, in particular, functions that help with the preparation of plain-text reports, e.g. for expanding and aligning strings that form the lines of such reports. The package also provides generic functions for transforming R objects to HTML and to plain text.

Computes a point pattern in R^2 or on a graph that is representative of a collection of many data patterns. The result is an approximate barycenter (also known as Fréchet mean or prototype) based on a transport-transform metric. Possible choices include Optimal SubPattern Assignment (OSPA) and Spike Time metrics. Details can be found in Müller, Schuhmacher and Mateu (2020) <doi:10.1007/s11222-020-09932-y>.

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.

Agglomerative hierarchical clustering with a bespoke distance measure based on medication similarities in the Anatomical Therapeutic Chemical Classification System, medication timing and medication amount or dosage. Tools for summarizing, illustrating and manipulating the cluster objects are also available.

Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.

Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code.

Feature selection algorithm that extracts features in highly correlated spaces. The extracted features are meant to be fed into simple explainable models such as linear or logistic regressions. The package is useful in the field of explainable modelling as a way to understand variable behavior.

This package provides a collection of clinical trial designs and methods, implemented in rstan and R, including: the Continual Reassessment Method by O'Quigley et al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and more. We provide functions to aid model-fitting and analysis. The rstan implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch.

Test the nullity of covariances, in a set of variables, using a simple univariate procedure. See Marques, Diago, Norouzirad, Bispo (2023) <doi:10.1002/mma.9130>.