This package provides a tool to help create shiny apps for selecting and annotating elements of images. Users must supply images, questions, and answer choices. The user interface is a dynamic shiny app, that displays the images and questions and answer choices. The data generated can be saved to a file that can be used for subsequent analysis. The original purpose was to annotate still images from tennis video for face recognition and emotion detection purposes.

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>.

You only need to type why pie charts are bad on Google to find thousands of articles full of (valid) reasons why other types of charts should be preferred over this one. Therefore, because of the little use due to the reasons already mentioned, making pie charts (and related) in R is not straightforward, so other functions are needed to simplify things. In this R package there are useful functions to make tasty pie charts immediately by exploiting the many cool templates provided.

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>.

It creates an invisible layer that allow to see the Seurat object as tibble and interact seamlessly with the tidyverse.

This package implements additional operators for computer vision models, including operators necessary for image segmentation and object detection deep learning models.

Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.

Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.

Download geographic shapes from the United States Census Bureau TIGER/Line Shapefiles <https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html>. Functions support downloading and reading in geographic boundary data. All downloads can be set up with a cache to avoid multiple downloads. Data is available back to 2000 for most geographies.

Density, distribution function, the quantile function, random generation function, and maximum likelihood estimation.

This package provides new layer functions to tmap for creating various types of cartograms. A cartogram is a type of thematic map in which geographic areas are resized or distorted based on a quantitative variable, such as population. The goal is to make the area sizes proportional to the selected variable while preserving geographic positions as much as possible.

This package provides functions for statistical analysis, prediction and control of time series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.

Using Gaussian graphical models we propose a novel approach to perform pathway analysis using gene expression. Given the structure of a graph (a pathway) we introduce two statistical tests to compare the mean and the concentration matrices between two groups. Specifically, these tests can be performed on the graph and on its connected components (cliques). The package is based on the method described in Massa M.S., Chiogna M., Romualdi C. (2010) <doi:10.1186/1752-0509-4-121>.

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

Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. TDApplied provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.

The textrank algorithm is an extension of the Pagerank algorithm for text. The algorithm allows to summarize text by calculating how sentences are related to one another. This is done by looking at overlapping terminology used in sentences in order to set up links between sentences. The resulting sentence network is next plugged into the Pagerank algorithm which identifies the most important sentences in your text and ranks them. In a similar way textrank can also be used to extract keywords. A word network is constructed by looking if words are following one another. On top of that network the Pagerank algorithm is applied to extract relevant words after which relevant words which are following one another are combined to get keywords. More information can be found in the paper from Mihalcea, Rada & Tarau, Paul (2004) <https://www.aclweb.org/anthology/W04-3252/>.

Collect marketing data from TikTok Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package implements an algorithm for Latent Dirichlet Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>, using style conventions from the tidyverse', Wickham et al. (2019)<doi:10.21105/joss.01686>, and tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>. Fitting is done via collapsed Gibbs sampling. Also implements several novel features for LDA such as guided models and transfer learning.

Get comments posted on YouTube videos, information on how many times a video has been liked, search for videos with particular content, and much more. You can also scrape captions from a few videos. To learn more about the YouTube API, see <https://developers.google.com/youtube/v3/>.

This package provides a suite of auxiliary functions that enhance time series estimation and forecasting, including a robust anomaly detection routine based on Chen and Liu (1993) <doi:10.2307/2290724> (imported and wrapped from the tsoutliers package), utilities for managing calendar and time conversions, performance metrics to assess both point forecasts and distributional predictions, advanced simulation by allowing the generation of time series componentsâ such as trend, seasonal, ARMA, irregular, and anomaliesâ in a modular fashion based on the innovations form of the state space model and a number of transformation methods including Box-Cox, Logit, Softplus-Logit and Sigmoid.

Efficient tabulation with Stata-like output. For each unique value of the variable, it shows the number of observations with that value, proportion of observations with that value, and cumulative proportion, in descending order of frequency. Accepts data.table, tibble, or data.frame as input. Efficient with big data: if you give it a data.table, tab() uses data.table syntax.

Create browsers for reading full texts from a token list format. Information obtained from text analyses (e.g., topic modeling, word scaling) can be used to annotate the texts.

Add tests in-line in examples. Provides standalone functions for facilitating easier test writing in Rd files. However, a more familiar interface is provided using roxygen2 tags. Tools are also provided for facilitating package configuration and use with testthat'.

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