Compute and visualize the Tissot Indicatrix for map projections. The indicatrix characterizes projection distortion by computing scale factors, angular deformation, areal distortion, and convergence at arbitrary points. Based on the calculations shared by Bill Huber on <https://gis.stackexchange.com/a/5075/482>. Uses GDAL for coordinate transformation. Developed using the method published in Snyder, JP (1987) <doi:10.3133/pp1395>.

Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>.).

This package provides a convenient R interface to the National Health Service NHS Technology Reference Update Distribution (TRUD) API', allowing users to list available releases for their subscribed items, retrieve metadata, and download release files. For more information on the API, see <https://isd.digital.nhs.uk/trud/users/guest/filters/0/api>.

We focus on the diagnostic ability assessment of medical tests when the outcome of interest is the status (alive or dead) of the subjects at a certain time-point t. This binary status is determined by right-censored times to event and it is unknown for those subjects censored before t. Here we provide three methods (unknown status exclusion, imputation of censored times and using time-dependent ROC curves) to evaluate the diagnostic ability of binary and continuous tests in this context. Two references for the methods used here are Skaltsa et al. (2010) <doi:10.1002/bimj.200900294> and Heagerty et al. (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.

For when your colors absolutely should not be excluded from the narrative.

Sometimes you need to split your data and work on the two chunks independently before bringing them back together. Taber allows you to do that with its two functions.

This package provides several confidence interval and testing procedures, based on either semiparametric (using event-specific win ratios) or nonparametric measures, including the ratio of integrated cumulative hazard (RICH) and the ratio of integrated transformed cumulative hazard (RITCH), for treatment effect inference with terminal and non-terminal events under competing risks. The semiparametric results were developed in Yang et al. (2022 <doi:10.1002/sim.9266>), and the nonparametric results were developed in Yang (2025 <doi:10.1002/sim.70205>). For comparison, results for the win ratio (Finkelstein and Schoenfeld 1999 <doi:10.1002/(SICI)1097-0258(19990615)18:11%3C1341::AID-SIM129%3E3.0.CO;2-7>), Pocock et al. 2012 <doi:10.1093/eurheartj/ehr352>, and Bebu and Lachin 2016 <doi:10.1093/biostatistics/kxv032>) are included. The package also supports univariate survival analysis with a single event. In this package, effect size estimates and confidence intervals are obtained for each event type, and several testing procedures are implemented for the global null hypothesis of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazards assumptions, under which the event-specific win ratios converge to hazard ratios, and a test of equal hazard ratios, are provided. For summarizing the treatment effect across all events, confidence intervals for linear combinations of the event-specific win ratios, RICH, or RITCH are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al. (2022 <doi:10.1002/sim.9266>) and Yang (2025 <doi:10.1002/sim.70205>).

Theme and colour palettes for The Globe and Mail's graphics. Includes colour and fill scale functions, colour palette helpers and a Globe-styled ggplot2 theme object.

Assists performing tip-dating of phylogenetic trees with BEAST BEAST is a popular software for phylogenetic analysis. The package assists the implementation of various phylogenetic tip- dating tests using BEAST. It contains two main functions. The first one allows preparing date randomization analyses, which assess the temporal signal of a data set. The second function allows performing leave-one-out analyses, which test for the consistency between independent calibration sequences and allow pinpointing those leading to potential bias. The included tutorial provides detailed step-by-step instructions. An expanded description of the package can be found in article: Rieux, A. and Khatchikian, C.E. (2017), TIPDATINGBEAST: an R package to assist the implementation of phylogenetic tip-dating tests using BEAST. Molecular Ecology Resources, 17: 608-613. <onlinelibrary.wiley.com/doi/full/10.1111/1755-0998.12603>.

Extension of the tidyverse for SpatRaster and SpatVector objects of the terra package. It includes also new geom_ functions that provide a convenient way of visualizing terra objects with ggplot2'.

This package provides a tidy workflow for generating, estimating, reporting, and plotting structural equation models using lavaan', OpenMx', or Mplus'. Throughout this workflow, elements of syntax, results, and graphs are represented as tidy data, making them easy to customize. Includes functionality to estimate latent class analyses, and to plot dagitty and igraph objects.

Compute arbitrary non-parametric bootstrap statistics on data in tidy data frames.

This package provides a general regression neural network (GRNN) is a variant of a Radial Basis Function Network characterized by a fast single-pass learning. tsfgrnn allows you to forecast time series using a GRNN model Francisco Martinez et al. (2019) <doi:10.1007/978-3-030-20521-8_17> and Francisco Martinez et al. (2022) <doi:10.1016/j.neucom.2021.12.028>. When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. You can consult and plot how the prediction was done. It is also possible to assess the forecasting accuracy of the model using rolling origin evaluation.

Obtain population density and body size structure, using video material or image sequences as input. Functions assist in the creation of image sequences from videos, background detection and subtraction, particle identification and tracking. An artificial neural network can be trained for noise filtering. The goal is to supply accurate estimates of population size, structure and/or individual behavior, for use in evolutionary and ecological studies.

Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.

R implementation of TFactS to predict which are the transcription factors (TFs), regulated in a biological condition based on lists of differentially expressed genes (DEGs) obtained from transcriptome experiments. This package is based on the TFactS concept by Essaghir et al. (2010) <doi:10.1093/nar/gkq149> and expands it. It allows users to perform TFactS'-like enrichment approach. The package can import and use the original catalogue file from the TFactS as well as users defined catalogues of interest that are not supported by TFactS (e.g., Arabidopsis).

Testing for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha & Song 2022) <doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension test quantifies the statistical evidence for trajectory presence. The subset specificity measure summarizes pattern heterogeneity using the minimum subtree cover. There is no user tunable parameters for either method. Examples are included to illustrate how to use the methods on single-cell data for studying gene and pathway expression dynamics and pathway expression specificity.

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.

An implementation of the Thornley transport resistance plant growth model. The package can be used to simulate plant growth as forced by climate system variables. The package provides methods for formatting forcing variables, simulating growth dynamics and calibrating model parameters. For more information see Higgins et al. (2025) TTR.PGM: An R package for modelling the distributions and dynamics of plants using the Thornley transport resistance plant growth model. Methods in Ecology and Evolution. in press.

Imports non-tabular from Excel files into R. Exposes cell content, position and formatting in a tidy structure for further manipulation. Tokenizes Excel formulas. Supports .xlsx and .xlsm via the embedded RapidXML C++ library <https://rapidxml.sourceforge.net>. Does not support .xlsb or .xls'.

The tsgc package provides comprehensive tools for the analysis and forecasting of epidemic trajectories. It is designed to model the progression of an epidemic over time while accounting for the various uncertainties inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach, using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in epidemic data. The reinitialization feature enhances the modelâ s ability to adapt to the emergence of new waves. The forecasts generated by the package are of value to public health officials and researchers who need to understand and predict the course of an epidemic to inform decision-making. Beyond its application in public health, the package is also a useful resource for researchers and practitioners in fields where the trajectories of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy. The core methodologies implemented in tsgc are based on well-established statistical techniques as described in Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021) <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, and Thamotheram (2024) <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.

Create Typst table markup from data frames. Features a pipe-friendly interface for column, row, and cell styling with support for grouped headers, grouped rows, and data-driven formatting.

This package provides a unified tidyverse-compatible interface to R's machine learning packages. Wraps established implementations from glmnet', randomForest', xgboost', e1071', rpart', gbm', nnet', cluster', dbscan', and others - providing consistent function signatures, tidy tibble output, and unified ggplot2'-based visualization. The underlying algorithms are unchanged; tidylearn simply makes them easier to use together. Access raw model objects via the $fit slot for package-specific functionality. Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>, LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, elastic net Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, support vector machines Cortes and Vapnik (1995) <doi:10.1007/BF00994018>, and gradient boosting Friedman (2001) <doi:10.1214/aos/1013203451>.

Implementation of the tree-guided feature selection and logic aggregation approach introduced in Chen et al. (2024) <doi:10.1080/01621459.2024.2326621>. The method enables the selection and aggregation of large-scale rare binary features with a known hierarchical structure using a convex, linearly-constrained regularized regression framework. The package facilitates the application of this method to both linear regression and binary classification problems by solving the optimization problem via the smoothing proximal gradient descent algorithm (Chen et al. (2012) <doi:10.1214/11-AOAS514>).