Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the networkDynamic format. This work was supported by grant R01HD68395 from the National Institute of Health.

Turn complex JSON data into tidy data frames.

General framework to organize data, methods, and results used in reproducible scientific analyses. A TAF analysis consists of four scripts (data.R, model.R, output.R, report.R) that are run sequentially. Each script starts by reading files from a previous step and ends with writing out files for the next step. Convenience functions are provided to version control the required data and software, run analyses, clean residues from previous runs, manage files, manipulate tables, and produce figures. With a focus on stability and reproducible analyses, the TAF package comes with no dependencies. TAF forms a base layer for the icesTAF package and other scientific applications.

Provide the core functionality to transform longitudinal data to complex-time (kime) data using analytic and numerical techniques, visualize the original time-series and reconstructed kime-surfaces, perform model based (e.g., tensor-linear regression) and model-free classification and clustering methods in the book Dinov, ID and Velev, MV. (2021) "Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics", De Gruyter STEM Series, ISBN 978-3-11-069780-3. <https://www.degruyter.com/view/title/576646>. The package includes 18 core functions which can be separated into three groups. 1) draw longitudinal data, such as Functional magnetic resonance imaging(fMRI) time-series, and forecast or transform the time-series data. 2) simulate real-valued time-series data, e.g., fMRI time-courses, detect the activated areas, report the corresponding p-values, and visualize the p-values in the 3D brain space. 3) Laplace transform and kimesurface reconstructions of the fMRI data.

The goal of TailID is to detect sensitive points in the tail of a dataset using techniques from Extreme Value Theory (EVT). It utilizes the Generalized Pareto Distribution (GPD) for assessing tail behavior and detecting inconsistent points with the Identical Distribution hypothesis of the tail. For more details see Manau (2025)<doi:10.4230/LIPIcs.ECRTS.2025.20>.

The tabularmap is one of the visualization methods for efficiently displaying data consisting of multiple elements by tiling them. When dealing with geospatial, it corrects for differences in visibility between areas.

Execution of various time series models and choosing the best one either by a specific error metric or by picking the best one by majority vote. The models are based on the "forecast" package, written by Prof. Rob Hyndman.

Collect your data on digital marketing campaigns from Taboola using the Windsor.ai API <https://windsor.ai/api-fields/>.

Calculates several thermal comfort indexes using temperature, wind speed and relative humidity values, calculating indexes such as Humidex, windchill, Discomfort Index and others.

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

The Taylor Russell model is a widely used method for assessing test validity in personnel selection tasks. The three functions in this package extend this model in a number of notable ways. TR() estimates test validity for a single selection test via the original Taylor Russell model. It extends this model by allowing users greater flexibility in argument choice. For example, users can specify any three of the four parameters (base rate, selection ratio, criterion validity, and positive predictive value) of the Taylor Russell model and estimate the remaining parameter (see the help file for examples). The TaylorRussell() function generalizes the original Taylor Russell model to allow for multiple selection tests (predictors). To our knowledge, this is the first generalization of the Taylor Russell model to allow for three or more selection tests (it is also the first to correctly handle models with two selection tests). TRDemo() is a shiny program for illustrating the underlying logic of the Taylor Russell model. Taylor, HC and Russell, JT (1939) "The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables" <doi:10.1037/h0057079>.

Some accelerated three-term conjugate gradient algorithms implemented purely in R with the same user interface as optim(). The search directions and acceleration scheme are described in Andrei, N. (2013) <doi:10.1016/j.amc.2012.11.097>, Andrei, N. (2013) <doi:10.1016/j.cam.2012.10.002>, and Andrei, N (2015) <doi:10.1007/s11075-014-9845-9>. Line search is done by a hybrid algorithm incorporating the ideas in Oliveia and Takahashi (2020) <doi:10.1145/3423597> and More and Thuente (1994) <doi:10.1145/192115.192132>.

This package provides a suite of functions for visualising ternary probabilistic forecasts, as discussed in the paper by Jupp (2012) <doi:10.1098/rsta.2011.0350>.

Base R sometimes requires verbose statements for simple, often recurring tasks, such as printing text without trailing space, ending with newline. This package aims at providing shorthands for such tasks.

This package implements Time-Weighted Dynamic Time Warping (TWDTW), a measure for quantifying time series similarity. The TWDTW algorithm, described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and Maus et al. (2019) <doi:10.18637/jss.v088.i05>, is applicable to multi-dimensional time series of various resolutions. It is particularly suitable for comparing time series with seasonality for environmental and ecological data analysis, covering domains such as remote sensing imagery, climate data, hydrology, and animal movement. The twdtw package offers a user-friendly R interface, efficient Fortran routines for TWDTW calculations, flexible time weighting definitions, as well as utilities for time series preprocessing and visualization.

Swift and seamless Single Sign-On (SSO) integration. Designed for effortless compatibility with popular Single Sign-On providers like Google and Microsoft, it streamlines authentication, enhancing both user experience and application security. Elevate your shiny applications for a simplified, unified, and secure authentication process.

Accompanies the texts Time Series for Data Science with R by Woodward, Sadler and Robertson & Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott. It is helpful for data analysis and for time series instruction.

Node centrality measures for temporal networks. Available measures are temporal degree centrality, temporal closeness centrality and temporal betweenness centrality defined by Kim and Anderson (2012) <doi:10.1103/PhysRevE.85.026107>. Applying the REN algorithm by Hanke and Foraita (2017) <doi:10.1186/s12859-017-1677-x> when calculating the centrality measures keeps the computational running time linear in the number of graph snapshots. Further, all methods can run in parallel up to the number of nodes in the network.

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>).

This package performs transformation discrimination analysis and non-transformation discrimination analysis. It also includes functions for Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Mixture Discriminant Analysis. In the context of mixture discriminant analysis, it offers options for both common covariance matrix (common sigma) and individual covariance matrices (uncommon sigma) for the mixture components.

Build customized transfer function and ARIMA models with multiple operators and parameter restrictions. Provides tools for model identification, estimation using exact or conditional maximum likelihood, diagnostic checking, automatic outlier detection, calendar effects, forecasting, and seasonal adjustment. The new version also supports unobserved component ARIMA model specification and estimation for structural time series analysis.

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

This package provides rolling statistical functions based on date and time windows instead of n-lagged observations.

Processing and analysis of pathomics, omics and other medical datasets. tRigon serves as a toolbox for descriptive and statistical analysis, correlations, plotting and many other methods for exploratory analysis of high-dimensional datasets.