Analysis of treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events. This package implements methods for estimating and inferring the cumulative incidence functions for time-to-event (TTE) outcomes with intercurrent events (ICE) under the five strategies outlined in the ICH E9 (R1) addendum, see Deng (2025) <doi:10.1002/sim.70091>. This package can be used for analyzing data from both randomized controlled trials and observational studies. In general, the data involve a primary outcome event and, potentially, an intercurrent event. Two data structures are allowed: competing risks, where only the time to the first event is recorded, and semicompeting risks, where the times to both the primary outcome event and intercurrent event (or censoring) are recorded. For estimation methods, users can choose nonparametric estimation (which does not use covariates) and semiparametrically efficient estimation.

Htmlwidget of Tippyjs to add tooltips to Shiny apps and R markdown documents.

Recursive partytioning of transformation models with corresponding random forest for conditional transformation models as described in Transformation Forests (Hothorn and Zeileis, 2021, <doi:10.1080/10618600.2021.1872581>) and Top-Down Transformation Choice (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).

STARMA (Space-Time Autoregressive Moving Average) models are commonly utilized in modeling and forecasting spatiotemporal time series data. However, the intricate nonlinear dynamics observed in many space-time rainfall patterns often exceed the capabilities of conventional STARMA models. This R package enables the fitting of Time Delay Spatio-Temporal Neural Networks, which are adept at handling such complex nonlinear dynamics efficiently. For detailed methodology, please refer to Saha et al. (2020) <doi:10.1007/s00704-020-03374-2>.

This package provides a universal non-uniform random number generator for quite arbitrary distributions with piecewise twice differentiable densities.

An R wrapper for the Spotify Web API <https://developer.spotify.com/web-api/>.

This package provides a tool to analyze and visualize toponym distributions. This package is intended as an interface to the GeoNames data. A regular expression filters data and in a second step a map is created displaying all locations in the filtered data set. The functions make data and plots available for further analysisâ either within R or in a chosen directory. Users can select regions within countries, provide coordinates to define regions, or specify a region within the package to restrict the data selection to that region or compare regions with the remainder of countries. This package relies on the R packages geodata for map data and ggplot2 for plotting purposes. For more information on the study of toponyms, see Wichmann & Chevallier (2025) <doi:10.5195/names.2025.2616>.

This package provides a wrapper for the TexTra API <https://mt-auto-minhon-mlt.ucri.jgn-x.jp/>, a web service for translating texts between different languages. TexTra API account is required to use the service.

Computes the product moments of the truncated multivariate normal distribution, particularly for cases involving patterned variance-covariance matrices. It also has the capability to calculate these moments with arbitrary positive-definite matrices, although performance may degrade for high-dimensional variables.

Visualisation, analysis and quality control of conversational data. Rapid and visual insights into the nature, timing and quality of time-aligned annotations in conversational corpora. For more details, see Dingemanse et al., (2022) <doi:10.18653/v1/2022.acl-long.385>.

This package provides native R implementations of the multiphase parametric hazard model of Blackstone, Naftel, and Turner (1986) <doi:10.1080/01621459.1986.10478314> with a focus on behavioral parity, transparent numerics, and reproducible validation against reference outputs from the original C'/'SAS HAZARD program, originally developed at the University of Alabama at Birmingham (UAB). The SAS'/'C code and this R package are currently developed and maintained at The Cleveland Clinic Foundation, and the R code was wholly developed at The Cleveland Clinic Foundation. The generalized temporal decomposition family extends to longitudinal mixed-effects settings (Rajeswaran et al. 2018 <doi:10.1177/0962280215623583>). The package is intentionally implemented in pure R first; performance-critical paths may later be accelerated with Rcpp without changing the public interface.

This package provides a set of vectorised functions to calculate medical equations used in transplantation, focused mainly on transplantation of abdominal organs. These functions include donor and recipient risk indices as used by NHS Blood & Transplant, OPTN/UNOS and Eurotransplant, tools for quantifying HLA mismatches, functions for calculating estimated glomerular filtration rate (eGFR), a function to calculate the APRI (AST to platelet ratio) score used in initial screening of suitability to receive a transplant from a hepatitis C seropositive donor and some biochemical unit converter functions. All functions are designed to work with either US or international units. References for the equations are provided in the vignettes and function documentation.

Perform a Visual Predictive Check (VPC), while accounting for stratification, censoring, and prediction correction. Using piping from magrittr', the intuitive syntax gives users a flexible and powerful method to generate VPCs using both traditional binning and a new binless approach Jamsen et al. (2018) <doi:10.1002/psp4.12319> with Additive Quantile Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS) prediction correction.

This comprehensive toolkit for T-distributed regression is designated as "TLIC" (The LIC for T Distribution Regression Analysis) analysis. It is predicated on the assumption that the error term adheres to a T-distribution. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.

Simulate phase II and/or phase III clinical trials. It supports various types of endpoints and adaptive strategies. Tools for carrying out graphical testing procedure and combination test under group sequential design are also provided.

This package provides functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press.

This package implements inverse and augmented inverse probability weighted estimators for common treatment effect parameters at an interim analysis with time-lagged outcome that may not be available for all enrolled subjects. Produces estimators, standard errors, and information that can be used to compute stopping boundaries using software that assumes that the estimators/test statistics have independent increments. Tsiatis, A. A. and Davidian, M., (2022) <doi:10.1002/sim.9580> .

Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell (2017) <doi:10.1021/acs.est.7b01613>.

This package provides utilities to create and use lenses to simplify data manipulation. Lenses are composable getter/setter pairs that provide a functional approach to manipulating deeply nested data structures, e.g., elements within list columns in data frames. The implementation is based on the earlier lenses R package <https://github.com/cfhammill/lenses>, which was inspired by the Haskell lens package by Kmett (2012) <https://github.com/ekmett/lens>, one of the most widely referenced implementations of lenses. For additional background and history on the theory of lenses, see the lens package wiki: <https://github.com/ekmett/lens/wiki/History-of-Lenses>.

This package implements a sequential panel estimation protocol for regional economic panels that combines three estimation layers in a fixed order. The first layer applies a two-way fixed effects baseline. The second layer applies the panel threshold regression method of Hansen (1999) <doi:10.1016/S0304-4076(99)00025-1> to identify structural breaks at an unknown threshold of a moderating variable, with bootstrap inference following Hansen (2000) <doi:10.1111/1468-0262.00124>. The third layer applies a spatial Durbin model with an impact decomposition following LeSage and Pace (2009, ISBN:978-1-4200-6424-7) to quantify direct and indirect spillover effects. The fourth layer applies the two-step panel quantile estimator of Canay (2011) <doi:10.1111/j.1368-423X.2011.00349.x> to document distributional heterogeneity in the outcome. The threshold identified in the second layer defines a subsample used as structured input to the fourth layer, and a consistency check evaluates whether the three sets of results are jointly compatible with a common underlying structural relationship. An illustrative panel of 33 districts of the state of Maharashtra, India, observed over 10 agricultural years, is included with the package.

The main goal of the R package treeDbalance is to provide functions for the computation of several measurements of 3D node imbalance and their respective 3D tree imbalance indices, as well as to introduce the new phylo3D format for rooted 3D tree objects. Moreover, it encompasses an example dataset of 3D models of 63 beans in phylo3D format. Please note that this R package was developed alongside the project described in the manuscript Measuring 3D tree imbalance of plant models using graph-theoretical approaches by M. Fischer, S. Kersting, and L. Kühn (2023) <doi:10.48550/arXiv.2307.14537>, which provides precise mathematical definitions of the measurements. Furthermore, the package contains several helpful functions, for example, some auxiliary functions for computing the ancestors, descendants, and depths of the nodes, which ensures that the computations can be done in linear time, or functions that convert existing formats of 3D tree models of other software into the phylo3D format. Moreover, it comprises functions to extract the graph-theoretical topology without vertices of in- and out-degree 1 of rooted 3D trees as well as to adapt node enumerations to the common phylo format. Most functions of treeDbalance require as input a rooted tree in the phylo3D format, an extended phylo format (as introduced in the R package ape 1.9 in November 2006). Such a phylo3D object must have at least two new attributes next to those required by the phylo format: node.coord', the coordinates of the nodes, as well as edge.weight', the literal weight or volume of the edges. Optional attributes are edge.diam', the diameter of the edges, and edge.length', the length of the edges. For visualization purposes one can also specify edge.type', which ranges from normal cylinder to bud to leaf, as well as edge.color to change the color of the edge depiction. This project was supported by the joint research project DIG-IT! funded by the European Social Fund (ESF), reference: ESF/14-BM-A55-0017/19, and the Ministry of Education, Science and Culture of Mecklenburg-Western Pomerania, Germany, as well as by the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM â Artificial Intelligence in Farming funded by the German Federal Ministry of Education and Research (FKZ: 03WIR4805).

This package provides tools for Topological Data Analysis. The package focuses on statistical analysis of persistent homology and density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries GUDHI <https://project.inria.fr/gudhi/software/>, Dionysus <https://www.mrzv.org/software/dionysus/>, and PHAT <https://bitbucket.org/phat-code/phat/>. This package also implements methods from Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2015) <doi:10.20382/jocg.v6i2a8> for analyzing the statistical significance of persistent homology features.

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 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>.