Specialized toolkit for processing biological and fisheries data from Peru's anchovy (Engraulis ringens) fishery. Provides functions to analyze fishing logbooks, calculate biological indicators (length-weight relationships, juvenile percentages), generate spatial fishing indicators, and visualize regulatory measures from Peru's Ministry of Production. Features automated data processing from multiple file formats, coordinate validation, spatial analysis of fishing zones, and tools for analyzing fishing closure announcements and regulatory compliance. Includes built-in datasets of Peruvian coastal coordinates and parallel lines for analyzing fishing activities within regulatory zones.

Helper functions for empirical research in financial economics, addressing a variety of topics covered in Scheuch, Voigt, and Weiss (2023) <doi:10.1201/b23237>. The package is designed to provide shortcuts for issues extensively discussed in the book, facilitating easier application of its concepts. For more information and resources related to the book, visit <https://www.tidy-finance.org/r/index.html>.

Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.

Cooperative game theory models decision-making situations in which a group of agents, called players, may achieve certain benefits by cooperating to reach an optimal outcome. It has great potential in different fields, since it offers a scenario to analyze and solve problems in which cooperation is essential to achieve a common goal. The TUGLab (Transferable Utility Games Laboratory) R package contains a set of scripts that could serve as a helpful complement to the books and other materials used in courses on cooperative game theory, and also as a practical tool for researchers working in this field. The TUGLab project was born in 2006 trying to highlight the geometrical aspects of the theory of cooperative games for 3 and 4 players. TUGlabWeb is an online platform on which the basic functions of TUGLab are implemented, and it is being used all over the world as a resource in degree, master's and doctoral programs. This package is an extension of the first versions and enables users to work with games in general (computational restrictions aside). The user can check properties of games, compute well-known games and calculate several set-valued and single-valued solutions such as the core, the Shapley value, the nucleolus or the core-center. The package also illustrates how the Shapley value flexibly adapts to various cooperative game settings, including weighted players and coalitions, a priori unions, and restricted communication structures. In keeping with the original philosophy of the first versions, special emphasis is placed on the graphical representation of the solution concepts for 3 and 4 players.

Plots ternary diagrams (simplex plots / Gibbs triangles) and Holdridge life zone plots <doi:10.1126/science.105.2727.367> using the standard graphics functions. Allows custom annotation, interpolating, contouring and scaling of plotting region. Includes a Shiny user interface for point-and-click ternary plotting. An alternative to ggtern', which uses the ggplot2 family of plotting functions.

Fit of a double additive cure survival model with time-varying covariates. The additive terms in the long- and short-term survival submodels, modelling the cure probability and the event timing for susceptible units, are estimated using Laplace P-splines. For more details, see Lambert and Kreyenfeld (2025) <doi:10.1093/jrsssa/qnaf035>.

This package provides functions for propensity score estimation and weighting for continuous exposures as described in Zhu, Y., Coffman, D. L., & Ghosh, D. (2015). A boosting algorithm for estimating generalized propensity scores with continuous treatments. Journal of Causal Inference, 3(1), 25-40. <doi:10.1515/jci-2014-0022>.

This package provides multiple water chemistry-based models and published empirical models in one standard format. As many models have been included as possible, however, users should be aware that models have varying degrees of accuracy and applicability. To learn more, read the references provided below for the models implemented. Functions can be chained together to model a complete treatment process and are designed to work in a tidyverse workflow. Models are primarily based on these sources: Benjamin, M. M. (2002, ISBN:147862308X), Crittenden, J. C., Trussell, R., Hand, D., Howe, J. K., & Tchobanoglous, G., Borchardt, J. H. (2012, ISBN:9781118131473), USEPA. (2001) <https://www.epa.gov/sites/default/files/2017-03/documents/wtp_model_v._2.0_manual_508.pdf>.

Archive and manage times series data from official statistics. The timeseriesdb package was designed to manage a large catalog of time series from official statistics which are typically published on a monthly, quarterly or yearly basis. Thus timeseriesdb is optimized to handle updates caused by data revision as well as elaborate, multi-lingual meta information.

An integrated suite of tools for creating, maintaining, and reusing FAIR (Findable, Accessible, Interoperable, Reusable) theories. Designed to support transparent and collaborative theory development, the package enables users to formalize theories, track changes with version control, assess pre-empirical coherence, and derive testable hypotheses. Aligning with open science principles and workflows, theorytools facilitates the systematic improvement of theoretical frameworks and enhances their discoverability and usability.

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.

Utilizing the logger framework to record events within a package, specific to teal family of packages. Supports logging namespaces, hierarchical logging, various log destinations, vectorization, and more.

The functions needed to perform tight clustering Algorithm.

Helper functions for TUFLOW FV models. Current functionality includes reading in and plotting output POINTS files and generating initial conditions based on point observations.

This package provides a traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.

Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the mgcv package to specify splines via the formula interface. See Thorson et al. (2025) <doi:10.1111/geb.70035> for more details.

Better looking call stacks after an error.

This package provides a robust and user-friendly solution for transliterating Ukrainian strings into Latin symbols.

The typicality and eccentricity data analysis (TEDA) framework was put forward by Angelov (2013) <DOI:10.14313/JAMRIS_2-2014/16>. It has been further developed into multiple different techniques since, and provides a non-parametric way of determining how similar an observation, from a process that is not purely random, is to other observations generated by the process. This package provides code to use the batch and recursive TEDA methods that have been published.

This package provides an intuitive interface for working with the competing risk endpoints. The package wraps the cmprsk package, and exports functions for univariate cumulative incidence estimates and competing risk regression. Methods follow those introduced in Fine and Gray (1999) <doi:10.1002/sim.7501>.

Greedy optimal subset selection for transformation models (Hothorn et al., 2018, <doi:10.1111/sjos.12291> ) based on the abess algorithm (Zhu et al., 2020, <doi:10.1073/pnas.2014241117> ). Applicable to models from packages tram and cotram'. Application to shift-scale transformation models are described in Siegfried et al. (2024, <doi:10.1080/00031305.2023.2203177>).

Implementation of target-controlled infusion algorithms for compartmental pharmacokinetic and pharmacokinetic-pharmacodynamic models. Jacobs (1990) <doi:10.1109/10.43622>; Marsh et al. (1991) <doi:10.1093/bja/67.1.41>; Shafer and Gregg (1993) <doi:10.1007/BF01070999>; Schnider et al. (1998) <doi:10.1097/00000542-199805000-00006>; Abuhelwa, Foster, and Upton (2015) <doi:10.1016/j.vascn.2015.03.004>; Eleveld et al. (2018) <doi:10.1016/j.bja.2018.01.018>.

Generalized estimating equations (GEE) are a popular choice for analyzing longitudinal binary outcomes. This package provides an interface for fitting GEE, currently for logistic regression, within the tern <https://cran.r-project.org/package=tern> framework (Zhu, Sabanés Bové et al., 2023) and tabulate results easily using rtables <https://cran.r-project.org/package=rtables> (Becker, Waddell et al., 2023). It builds on geepack <doi:10.18637/jss.v015.i02> (Højsgaard, Halekoh and Yan, 2006) for the actual GEE model fitting.

Prebuilt shiny modules containing tools for the generation of rmarkdown reports, supporting reproducible research and analysis.