Create structured, formatted HTML tables of in a flexible and convenient way.

The TWN-list (Taxa Waterbeheer Nederland) is the Dutch standard for naming taxons in Dutch Watermanagement. This package makes it easier to use the TWN-list for ecological analyses. It consists of two parts. First it makes the TWN-list itself available in R. Second, it has a few functions that make it easy to perform some basic and often recurring tasks for checking and consulting taxonomic data from the TWN-list.

Implementation of target diagrams using lattice and ggplot2 graphics. Target diagrams provide a graphical overview of the respective contributions of the unbiased RMSE and MBE to the total RMSE (Jolliff, J. et al., 2009. "Summary Diagrams for Coupled Hydrodynamic-Ecosystem Model Skill Assessment." Journal of Marine Systems 76: 64â 82.).

This package implements an algorithm for generating maps, known as tile maps, in which each region is represented by a single tile of the same shape and size. The algorithm was first proposed in "Generating Tile Maps" by Graham McNeill and Scott Hale (2017) <doi:10.1111/cgf.13200>. Functions allow users to generate, plot, and compare square or hexagon tile maps.

This package provides a toolkit of tidy data manipulation verbs with data.table as the backend. Combining the merits of syntax elegance from dplyr and computing performance from data.table', tidyfst intends to provide users with state-of-the-art data manipulation tools with least pain. This package is an extension of data.table'. While enjoying a tidy syntax, it also wraps combinations of efficient functions to facilitate frequently-used data operations.

Fit Thurstonian Item Response Theory (IRT) models in R. This package supports fitting Thurstonian IRT models and its extensions using Stan', lavaan', or Mplus for the model estimation. Functionality for extracting results, making predictions, and simulating data is provided as well. References: Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>; Bürkner et al. (2019) <doi:10.1177/0013164419832063>.

This package provides functions such as str_crush(), add_missing_column(), coalesce_data() and drop_na_all() that complement tidyverse functionality or functions that provide alternative behaviors such as if_else2() and str_detect2().

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.

Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.

This package provides a bioinformatics tool for the estimation of the tumor purity from sequencing data. It uses the set of putative clonal somatic single nucleotide variants within copy number neutral segments to call tumor cellularity.

Facilities to work with vector and raster data in efficient repeatable and systematic work flow. Missing functionality in existing packages is included here to allow extraction from raster data with simple features and Spatial types and to make extraction consistent and straightforward. Extract cell numbers from raster data and return the cells as a data frame rather than as lists of matrices or vectors. The functions here allow spatial data to be used without special handling for the format currently in use.

This package creates a local database of many commonly used taxonomic authorities and provides functions that can quickly query this data.

This package implements the Topic Testlet Model (TTM) as described by Xiong et al. (2025) <doi:10.1111/jedm.70001>. The package integrates Latent Dirichlet Allocation (LDA) with the Partial Credit Model to account for local item dependence in testlets using latent topics from student textual responses.

This package provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. Heckbert (1995) <https://www.mgarland.org/files/papers/scape.pdf> using code from the hmm library written by Michael Fogleman <https://github.com/fogleman/hmm>.

This package provides an extensible formula system to quickly and easily create production quality tables. The processing steps are a formula parser, statistical content generation from data as defined by formula, followed by rendering into a table. Each step of the processing is separate and user definable thus creating a set of composable building blocks for highly customizable table generation. A user is not limited by any of the choices of the package creator other than the formula grammar. For example, one could chose to add a different S3 rendering function and output a format not provided in the default package, or possibly one would rather have Gini coefficients for their statistical content in a resulting table. Routines to achieve New England Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are provided. The package contains rendering for HTML5, Rmarkdown and an indexing format for use in tracing and tracking are provided.

Plot official statistics time series conveniently: automatic legends, highlight windows, stacked bar chars with positive and negative contributions, sum-as-line option, two y-axes with automatic horizontal grids that fit both axes and other popular chart types. tstools comes with a plethora of defaults to let you plot without setting an abundance of parameters first, but gives you the flexibility to tweak the defaults. In addition to charts, tstools provides a super fast, data.table backed time series I/O that allows the user to export / import long format, wide format and transposed wide format data to various file types.

Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) <doi:10.3390/genes13112036>. TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.

Convert semi-structured log files (such as Apache access.log files) into a tabular format (data.frame) using a standard template system.

Gene and exon information from Ensembl genome builds GRCh38.p13 (104) and GRCh37 (v40) to use with the topr package.

This package provides tools for measuring similarity among documents and detecting passages which have been reused. Implements shingled n-gram, skip n-gram, and other tokenizers; similarity/dissimilarity functions; pairwise comparisons; minhash and locality sensitive hashing algorithms; and a version of the Smith-Waterman local alignment algorithm suitable for natural language.

This package provides a tidy set of functions for summarising data, including descriptive statistics, frequency tables with normality testing, and group-wise significance testing. Designed for fast, readable, and easy exploration of both numeric and categorical data.

This package provides a coherent interface for evaluating models fit with the trending package. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. Users can obtain optimal parameters on a variety of loss functions, or use fixed ones (Kourenztes (2014) <doi:10.1016/j.ijpe.2014.06.007>). Intermittent time series classification methods and iMAPA that uses multiple temporal aggregation levels are also provided (Petropoulos & Kourenztes (2015) <doi:10.1057/jors.2014.62>).

This package provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.