Enables users to build ToxPi prioritization models and provides functionality within the grid framework for plotting ToxPi graphs. toxpiR allows for more customization than the ToxPi GUI (<https://toxpi.github.io/>) and integration into existing workflows for greater ease-of-use, reproducibility, and transparency. toxpiR package behaves nearly identically to the GUI; the package documentation includes notes about all differences. The vignettes download example files from <https://github.com/ToxPi/ToxPi-example-files>.

The ToxCast Data Analysis Pipeline ('tcpl') is an R package that manages, curve-fits, plots, and stores ToxCast data to populate its linked MySQL database, invitrodb'. The package was developed for the chemical screening data curated by the US EPA's Toxicity Forecaster (ToxCast) program, but tcpl can be used to support diverse chemical screening efforts.

Finding the best values for user-specified arguments of a prediction algorithm can be difficult, particularly if there is an interaction between argument levels. This package automates the testing of any user-defined prediction algorithm over an arbitrary number of arguments. It includes functions for testing the algorithm over the given arguments with respect to an arbitrary number of user-defined diagnostics, visualising the results of these tests, and finding the optimal argument combinations with respect to each diagnostic.

Tidying functions built on data.table to provide quick and efficient data manipulation with minimal overhead.

This package provides a set of functions that allow users for styling their R code according to the tidyverse style guide. The package uses a native Rust implementation to ensure the highest performance. Learn more about tergo at <https://rtergo.pagacz.io>.

Enables the acquisition of Korean financial market data, designed to integrate seamlessly with the tidyquant package.

Algorithms for detecting population structure from the history of coalescent events recorded in phylogenetic trees. This method classifies each tip and internal node of a tree into disjoint sets characterized by similar coalescent patterns.

Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance.

Prebuilt shiny modules containing tools for viewing data, visualizing data, understanding missing and outlier values within your data and performing simple data analysis. This extends teal framework that supports reproducible research and analysis.

Converting text to numerical features requires specifically created procedures, which are implemented as steps according to the recipes package. These steps allows for tokenization, filtering, counting (tf and tfidf) and feature hashing.

Generates publication-ready summary tables for clinical research, supporting descriptive summaries and comparisons across two or three groups. The package streamlines the analytical workflow by detecting variable types and applying appropriate statistical tests (Welch t-test, Wilcoxon rank-sum, Welch ANOVA, Kruskal-Wallis, Chi-squared, or Fisher's exact test). Results are formatted as tibble objects and can be exported to Word or Excel using the officer', flextable', and writexl packages. Optional pairwise post-hoc testing for three-group comparisons (Games-Howell and Dunn's test) is available via the rstatix package. Example data are derived from the landmark adjuvant colon cancer trial described in Moertel et al. (1990) <doi:10.1056/NEJM199002083220602>.

It performs the smoothing approach provided by penalized least squares for univariate and bivariate time series, as proposed by Guerrero (2007) and Gerrero et al. (2017). This allows to estimate the time series trend by controlling the amount of resulting (joint) smoothness. --- Guerrero, V.M (2007) <DOI:10.1016/j.spl.2007.03.006>. Guerrero, V.M; Islas-Camargo, A. and Ramirez-Ramirez, L.L. (2017) <DOI:10.1080/03610926.2015.1133826>.

To provide a high dimensional grouped variable selection approach for detection of whole-genome SNP effects and SNP-SNP interactions, as described in Fang et al. (2017, under review).

Variant determination and genotyping from high throughput sequences from multilocus amplicon libraries, typically sequenced in Illumina MiSeq or similar. It provides a set of core functions for the central steps: demultiplex by locus, truncate reads, variant calling, and genotype calling. Additionally, it provides a set of functions for diagnosis and estimation of best running parameters and multiple extensions for genotype/variants manipulation and reformatting. Output variants and genotypes are output in tidy format, thus facilitating reformatting, manipulation and potential connection to other R packages.

Email Finder R Client Library. Search emails are based on the website You give one domain name and it returns all the email addresses found on the internet. Email Finder generates or retrieves the most likely email address from a domain name, a first name and a last name. Email verify checks the deliverability of a given email address, verifies if it has been found in our database, and returns their sources.

The total deviation index (TDI) is an unscaled statistical measure used to evaluate the deviation between paired quantitative measurements when assessing the extent of agreement between different raters. It describes a boundary such that a large specified proportion of the differences in paired measurements are within the boundary (Lin, 2000) <https://pubmed.ncbi.nlm.nih.gov/10641028/>. This R package implements some methodologies existing in the literature for TDI estimation and inference in the case of two raters.

Two-stage procedure compares hazard rate functions, which may or may not cross each other.

Test your data! An extension of the testthat unit testing framework with a family of functions and reporting tools for checking and validating data frames.

Efficient estimation of the population-level causal effects of stochastic interventions on a continuous-valued exposure. Both one-step and targeted minimum loss estimators are implemented for the counterfactual mean value of an outcome of interest under an additive modified treatment policy, a stochastic intervention that may depend on the natural value of the exposure. To accommodate settings with outcome-dependent two-phase sampling, procedures incorporating inverse probability of censoring weighting are provided to facilitate the construction of inefficient and efficient one-step and targeted minimum loss estimators. The causal parameter and its estimation were first described by DÃ az and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust estimation procedure and its application to data from two-phase sampling designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert, and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package implementation is described in NS Hejazi and DC Benkeser (2020) <doi:10.21105/joss.02447>. Estimation of nuisance parameters may be enhanced through the Super Learner ensemble model in sl3', available for download from GitHub using remotes::install_github("tlverse/sl3")'.

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.

The goal of tor (to-R) is to help you to import multiple files from a single directory at once, and to do so as quickly, flexibly, and simply as possible.

This package provides a framework for high-dimensional mediation analysis using transfer learning. The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) <doi:10.1093/bib/bbaf460>.

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.

Tautulli (<http://tautulli.com>) is a monitoring application for Plex Media Servers (<https://www.plex.tv>) which collects a lot of data about media items and server usage such as play counts. This package interacts with the Tautulli API of any specified server to get said data into R. The Tautulli API documentation is available at <https://github.com/Tautulli/Tautulli/blob/master/API.md>.