This package provides a pilot matching design to automatically stratify and match large datasets. The manual_stratify() function allows users to manually stratify a dataset based on categorical variables of interest, while the auto_stratify() function does automatically by allocating a held-aside (pilot) data set, fitting a prognostic score (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based on prognostic score quantiles. The strata_match() function then does optimal matching of the data set in parallel within strata.

This package provides routines to check identifiability of linear structural equation models and factor analysis models. The routines are based on the graphical representation of structural equation models.

Transcribe audio files using the Speechmatics speech-to-text API <https://www.speechmatics.com/>. Supports custom vocabulary, speaker diarization, punctuation control, and audio filtering.

An interface to access data from Substack publications via API. Users can fetch the latest, top, search for specific posts, or retrieve a single post by its slug. This functionality is useful for developers and researchers looking to analyze Substack content or integrate it into their applications. For more information, visit the API documentation at <https://substackapi.dev/introduction>.

Collection of datasets from Sen & Srivastava: "Regression Analysis, Theory, Methods and Applications", Springer. Sources for individual data files are more fully documented in the book.

This package provides a pipeline-friendly toolkit for assembling stop motion animations from sequences of still images. Provides functions to read image directories, restructure frame sequences (duplicate, splice, arrange), apply per-frame pixel transformations (rotate, wiggle, flip, flop, blur, scale, crop, trim, border, background), and export the result as a GIF. All transformation functions accept a frames argument to target any subset of frames, bridging the gap between magick functions that operate on an entire image stack and fine-grained stop motion editing. Image processing is performed via ImageMagick Studio LLC (2024) <https://imagemagick.org>.

Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2019) <doi:10.1111/2041-210X.13215>). It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks, and perform data-stream randomization methods suitable for GPS data. Also provides measures of intragroup social dynamics including distance and direction to leaders, centroids and nearest neighbours.

This package provides a lightweight runtime type system for R that enables developers to declare and enforce variable types during execution. Inspired by TypeScript', the package introduces intuitive syntax for annotating variables and validating data structures, helping catch type-related errors early and making R code more robust and easier to maintain.

Get programmatic access to data from the Czech public budgeting and accounting database, Státnà pokladna <https://monitor.statnipokladna.gov.cz/>.

This package provides facilities to implement and run population models of stage-structured species...

This package provides a graph community detection algorithm that aims to be performant on large graphs and robust, returning consistent results across runs. SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023) <doi:10.1186/s13059-023-03062-0>. The core algorithm is written in C', providing speed and keeping the memory requirements low. This implementation can take advantage of multiple computing cores without increasing memory usage. SE2 can detect community structure across scales, making it a good choice for biological data, which often has hierarchical structure. Graphs can be passed to the algorithm as adjacency matrices using base R matrices, the Matrix library, igraph graphs, or any data that can be coerced into a matrix.

This package provides functionality to extract reactive expressions from a shiny application and convert them into stand-alone R scripts. This enables users to reproduce tables and visualisations outside the interactive UI, facilitating integration into static reports or automated workflows without requiring access to the original application source code.

Affords researchers the ability to draw stratified samples from the U.S. Department of Veteran's Affairs/Department of Defense Identity Repository (VADIR) database according to a variety of population characteristics. The VADIR database contains information for all veterans who were separated from the military after 1980. The central utility of the present package is to integrate data cleaning and formatting for the VADIR database with the stratification methods described by Mahto (2019) <https://CRAN.R-project.org/package=splitstackshape>. Data from VADIR are not provided as part of this package.

This package creates a data specification that describes the columns of a table (data.frame). Provides methods to read, write, and update the specification. Checks whether a table matches its specification. See specification.data.frame(),read.spec(), write.spec(), as.csv.spec(), respecify.character(), and %matches%.data.frame().

The objective of these functions is to derive a species assemblage that satisfies a functional trait profile. Restoring resilient ecosystems requires a flexible framework for selecting assemblages that are based on the functional traits of species. However, current trait-based models have been limited to algorithms that can only select species by optimising specific trait values, and could not elegantly accommodate the common desire among restoration ecologists to produce functionally diverse assemblages. We have solved this problem by applying a non-linear optimisation algorithm that optimises Rao Q, a closed-form functional trait diversity index that incorporates species abundances, subject to other linear constraints. This framework generalises previous models that only optimised the entropy of the community, and can optimise both functional diversity and entropy simultaneously. This package can also be used to generate experimental assemblages to test the effects of community-level traits on community dynamics and ecosystem function. The method is based on theory discussed in Laughlin (2014, Ecology Letters) and Laughlin et al. (2018, Methods in Ecology and Evolution).

Detect libraries used in a project and automatically create software bibliographies in PDF', Word', Rmarkdown', and BibTeX formats.

This package provides a customizable timer widget for shiny applications. Key features include countdown and count-up mode, multiple display formats (including simple seconds, minutes-seconds, hours-minutes-seconds, and minutes-seconds-centiseconds), ability to pause, resume, and reset the timer. shinytimer widget can be particularly useful for creating interactive and time-sensitive applications, tracking session times, setting time limits for tasks or quizzes, and more.

This package contains human behaviour datasets collected by the SAMPLING project (<https://sampling.warwick.ac.uk>).

This package provides functions to read and write ESRI shapefiles.

The implementation of SHAPBoost, a boosting-based feature selection technique that ranks features iteratively based on Shapley values.

This package provides a coherent interface for exploring and transforming multiple related data frames that share a common structure. Complements single-dataset inspection tools by operating across an entire collection at once. Also includes lightweight utilities for related file and folder management tasks.

This package provides a collection of functions for estimating spatial and spatio-temporal regression models. Moran eigenvectors are used as spatial basis functions to efficiently approximate spatially dependent Gaussian processes (i.e., random effects eigenvector spatial filtering; see Murakami and Griffith 2015 <doi: 10.1007/s10109-015-0213-7>). The implemented models include linear regression with residual spatial dependence, spatially/spatio-temporally varying coefficient models (Murakami et al., 2017, 2024; <doi:10.1016/j.spasta.2016.12.001>,<doi:10.48550/arXiv.2410.07229>), spatially filtered unconditional quantile regression (Murakami and Seya, 2019 <doi:10.1002/env.2556>), Gaussian and non-Gaussian spatial mixed models through compositionally-warping (Murakami et al. 2021, <doi:10.1016/j.spasta.2021.100520>).

The steepness package computes steepness as a property of dominance hierarchies. Steepness is defined as the absolute slope of the straight line fitted to the normalized David's scores. The normalized David's scores can be obtained on the basis of dyadic dominance indices corrected for chance or by means of proportions of wins. Given an observed sociomatrix, it computes hierarchy's steepness and estimates statistical significance by means of a randomization test.

Monte Carlo simulations of a game-theoretic model for the legal exemption system of the European cartel law are implemented in order to estimate the (mean) deterrent effect of this system. The input and output parameters of the simulated cartel opportunities can be visualized by three-dimensional projections. A description of the model is given in Moritz et al. (2018) <doi:10.1515/bejeap-2017-0235>.