This package provides a variety of tools for assessing dose response curves, with an emphasis on toxicity test data. The main feature of this package are modular functions which can be combined through the namesake pipeline, runtoxdrc', to automate the analysis for large and complex datasets. This includes optional data preprocessing steps, like outlier detection, solvent effects, blank correction, averaging technical replicates, and much more. Additionally, this pipeline is adaptable to any long form dataset, and does not require specific column or group naming to work.

This package provides a lightweight toolkit that provides functions for printing tables from input data in the R console or terminal with customizable formatting. Supported outputs include American Psychological Association (APA)-style tables (American Psychological Association, 2020, ISBN:9781433832178), correlation matrices, contingency tables, and two-column summary tables.

Information on all of the TriMet stops in the Portland Metro Area. It includes information such as the longitude, latitude, cross street, and direction of the stop. TriMet has catalogued these stops, 6880 in total.

Tests the hypothesis that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test. The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) \doi10.1016/j.csda.2010.04.012.

Covers k-table control analysis using multivariate control charts for qualitative variables using fundamentals of multiple correspondence analysis and multiple factor analysis. The graphs can be shown in a flat or interactive way, in the same way all the outputs can be shown in an interactive shiny panel.

For writing tables with custom formats in a Excel file ready to be distributed.

Simplifies access to Tunisian government open data from <https://data.gov.tn/fr/>. Queries datasets by theme, author, or keywords, retrieves metadata, and gets structured results ready for analysis; all through the official CKAN API.

This package provides a novel and fast two stage method for simultaneous multiple change point detection and variable selection for piecewise stationary autoregressive (PSAR) processes and linear regression model. It also simultaneously performs variable selection for each autoregressive model and hence the order selection.

Automates documentation of test_that() calls within R test files. The package scans test sources, extracts human-readable test titles (even when composed with functions like paste() or glue::glue(), ... etc.), and generates reproducible roxygen2-style listings that can be inserted both globally and per-section. It ensures idempotent updates and supports customizable numbering templates with hierarchical indices. Designed for developers, QA teams, and package maintainers seeking consistent, self-documenting test inventories.

Bringing business and financial analysis to the tidyverse'. The tidyquant package provides a convenient wrapper to various xts', zoo', quantmod', TTR and PerformanceAnalytics package functions and returns the objects in the tidy tibble format. The main advantage is being able to use quantitative functions with the tidyverse functions including purrr', dplyr', tidyr', ggplot2', lubridate', etc. See the tidyquant website for more information, documentation and examples.

Collection of functions that allow to export data frames to excel workbook.

Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of heterogeneous sources into a three-dimensional matrix of signals (features by observations by sources). The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) <DOI:10.1038/s41467-019-11052-9>.

This package implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) <doi:10.48550/arXiv.2506.19988>. Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses.

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>.

Construction of the Total Operating Characteristic (TOC) Curve and the Receiver (aka Relative) Operating Characteristic (ROC) Curve for spatial and non-spatial data. The TOC method is a modification of the ROC method which measures the ability of an index variable to diagnose either presence or absence of a characteristic. The diagnosis depends on whether the value of an index variable is above a threshold. Each threshold generates a two-by-two contingency table, which contains four entries: hits (H), misses (M), false alarms (FA), and correct rejections (CR). While ROC shows for each threshold only two ratios, H/(H + M) and FA/(FA + CR), TOC reveals the size of every entry in the contingency table for each threshold (Pontius Jr., R.G., Si, K. 2014. <doi:10.1080/13658816.2013.862623>).

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

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

Find out who maintains the packages you use in your current session or in your package library and maybe say thank you'.

Forecasting competitions are of increasing importance as a mean to learn best practices and gain knowledge. Data leakage is one of the most common issues that can often be found in competitions. Data leaks can happen when the training data contains information about the test data. For example: randomly chosen blocks of time series are concatenated to form a new time series, scale-shifts, repeating patterns in time series, white noise is added in the original time series to form a new time series, etc. tsdataleaks package can be used to detect data leakages in a collection of time series.

This package provides a wrapper for The Cancer Imaging Archive's REST API. The Cancer Imaging Archive (TCIA) hosts de-identified medical images of cancer available for public download, as well as rich metadata for each image series. TCIA provides a REST API for programmatic access to the data. This package provides simple functions to access each API endpoint. For more information, see <https://github.com/pamelarussell/TCIApathfinder> and TCIA's website.

Trelliscope is a scalable, flexible, interactive approach to visualizing data (Hafen, 2013 <doi:10.1109/LDAV.2013.6675164>). This package provides methods that make it easy to create a Trelliscope display specification for TrelliscopeJS. High-level functions are provided for creating displays from within tidyverse or ggplot2 workflows. Low-level functions are also provided for creating new interfaces.

This package provides test statistics, p-value, and confidence intervals based on 9 hypothesis tests for dependence.

This package provides methods for low-rank tensor regression with tensor-valued predictors and scalar covariates. Model estimation is performed using stochastic optimization with random-walk updates for low-rank factor matrices. Computationally intensive components for coefficient estimation and prediction are implemented in C++ via Rcpp'. The package also includes tools for cross-validation and prediction error assessment.

Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates.