This package provides classes for storing and manipulating taxonomic data. Most of the classes can be treated like base R vectors (e.g. can be used in tables as columns and can be named). Vectorized classes can store taxon names and authorities, taxon IDs from databases, taxon ranks, and other types of information. More complex classes are provided to store taxonomic trees and user-defined data associated with them.

Getting TikTok data (<https://www.tiktok.com/>) through the official and unofficial APIsâ in other words, you can track TikTok'.

Translation of logit models coefficients into percentages, following Deauvieau (2010) <doi:10.1177/0759106309352586>.

This package implements methods for selecting the number of factors in Poisson factor models, with a primary focus on Thinning Cross-Validation (TCV). The TCV method is based on the data thinning technique, which probabilistically partitions each count observation into training and test sets while preserving the underlying factor structure. The Poisson factor model is then fit on the training set, and model selection is performed by comparing predictive performance on the test set. This toolkit is designed for researchers working with high-dimensional count data in fields such as genomics, text mining, and social sciences. The data thinning methodology is detailed in Dharamshi et al. (2025) <doi:10.1080/01621459.2024.2353948> and Wang et al. (2025) <doi:10.1080/01621459.2025.2546577>.

The goal of this package will be to provide a simple interface for automatic machine learning that fits the tidymodels framework. The intention is to work for regression and classification problems with a simple verb framework.

The modern database TileDB introduces a powerful on-disk format for storing and accessing any complex data based on multi-dimensional arrays. It supports dense and sparse arrays, dataframes and key-values stores, cloud storage ('S3', GCS', Azure'), chunked arrays, multiple compression, encryption and checksum filters, uses a fully multi-threaded implementation, supports parallel I/O, data versioning ('time travel'), metadata and groups. It is implemented as an embeddable cross-platform C++ library with APIs from several languages, and integrations. This package provides the R support.

An object model for source text and translations. Find and extract translatable strings. Provide translations and seamlessly retrieve them at runtime.

This package creates a table of descriptive statistics for factor and numeric columns in a data frame. Displays these by groups, if any. Highly customizable, with support for html and pdf provided by kableExtra'. Respects original column order, column labels, and factor level order. See ?tablet.data.frame and vignettes.

With the objective of including data from RSS feeds into your analysis, tidyRSS parses RSS, Atom and JSON feeds and returns a tidy data frame.

Handles truncated members from the exponential family of probability distributions. Contains functions such as rtruncnorm() and dtruncpois(), which are truncated versions of rnorm() and dpois() from the stats package that also offer richer output containing, for example, the distribution parameters. It also provides functions to retrieve the original distribution parameters from a truncated sample by maximum-likelihood estimation.

Create highly customized tables with this simple and dependency-free package. Data frames can be converted to HTML', LaTeX', Markdown', Word', PNG', PDF', or Typst tables. The user interface is minimalist and easy to learn. The syntax is concise. HTML tables can be customized using the flexible Bootstrap framework, and LaTeX code with the tabularray package.

Simplify reporting many tables by creating tibbles of tables. With tabtibble', a tibble of tables is created with captions and automatic printing using knit_print()'.

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.

This package provides functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.

Carries out analyses of two-way tables with one observation per cell, together with graphical displays for an additive fit and a diagnostic plot for removable non-additivity via a power transformation of the response. It implements Tukey's Exploratory Data Analysis (1973) <ISBN: 978-0201076165> methods, including a 1-degree-of-freedom test for row*column non-additivity', linear in the row and column effects.

Time series toolkit with identical behavior for all time series classes: ts','xts', data.frame', data.table', tibble', zoo', timeSeries', tsibble', tis or irts'. Also converts reliably between these classes.

This package implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. Using marginal structural models, it can estimate intention-to-treat and per-protocol effects in emulated trials using electronic health records. A description and application of the method can be found in Danaei et al (2013) <doi:10.1177/0962280211403603>.

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.

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.

This package provides a set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. TensorPreAve provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. See Chen and Lam (2023) <arXiv:2208.04012> for more details.

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.

Attain excellent covariate balance by matching two treated units and one control unit or vice versa within strata. Using such triples, as opposed to also allowing pairs of treated and control units, allows easier interpretation of the two possible weights of observations and better insensitivity to unmeasured bias in the test statistic. Using triples instead of matching in a fixed 1:2 or 2:1 ratio allows for the match to be feasible in more situations. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from GitHub at <https://github.com/josherrickson/rrelaxiv/>. The Gurobi commercial optimization software is required to use the two functions [infsentrip()] and [triplesIP()]. These functions are not essential to the main purpose of this package. A free academic license can be obtained at <https://www.gurobi.com/features/academic-named-user-license/>. The gurobi R package can then be installed following the instructions at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.

This package provides a set of exploratory data analysis (EDA) tools for visualizing trends, diagnosing data types for beginner-friendly workflows, and automatically routing to suitable statistical tests or trend exploration models. Includes unified plotting functions for trend lines, grouped boxplots, and comparative scatterplots; automated statistical testing (e.g., t-test, Wilcoxon, ANOVA, Kruskal-Wallis, Tukey, Dunn) with optional effect size calculation; and model-based trend analysis using generalized additive models (GAM) for count data, generalized linear models (GLM) for continuous data, and zero-inflated models (ZIP/ZINB) for count data with potential zero-inflation. Also supports time-window continuity checks, cross-year handling in compare_monthly_cases(), and ARIMA-ready preparation with stationarity diagnostics, ensuring consistent parameter styles for reproducible research and user-friendly workflows.Methods are based on R Core Team (2024) <https://www.R-project.org/>, Wood, S.N.(2017, ISBN:978-1498728331), Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>, Simon Jackman (2024) <https://github.com/atahk/pscl/>, Achim Zeileis, Christian Kleiber, Simon Jackman (2008) <doi:10.18637/jss.v027.i08>.

This package provides a simple approach for constructing dynamic materials modeling suggested by Prasad and Gegel (1984) <doi:10.1007/BF02664902>. It can easily generate various processing-maps based on this model as well. The calculation result in this package contains full materials constants, information about power dissipation efficiency factor, and rheological properties, can be exported completely also, through which further analysis and customized plots will be applicable as well.