This package provides a statistical hypothesis test for conditional independence. Given residuals from a sufficiently powerful regression, it tests whether the covariance of the residuals is vanishing. It can be applied to both discretely-observed functional data and multivariate data. Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas Peters (2022) <doi:10.1111/rssb.12544>.

Addresses the log of zero by developing a new family of estimators called iterated Ordinary Least Squares. This family nests standard approaches such as log-linear and Poisson regressions, offers several computational advantages, and corresponds to the correct way to perform the popular log(Y + 1) transformation. For more details about how to use it, see the notebook at: <https://www.davidbenatia.com/>.

This package provides tools for searching, extracting and recoding the Intergovernmental Organizations ('IGO') Database (version 3), distributed by the Correlates of War Project <https://correlatesofwar.org/>. Includes IGO-year and country-year membership data, state system data and functions for deriving dyad-year joint membership results. For a description of the data, see Pevehouse, J. C. et al. (2020) <doi:10.1177/0022343319881175>.

This package provides list-processing utilities inspired by the SRFI-1 list library for Scheme (<https://srfi.schemers.org/srfi-1/srfi-1.html>), including car/cdr family accessors, zip, pairwise, for.each, pair.fold.right and friends. Higher-order helpers that are orthogonal to list processing are deferred to the functional package; this package is freely a mixture of implementation and API.

This package implements the efficient algorithm by Ortmann and Brandes (2017) <doi:10.1007/s41109-017-0027-2> to compute the orbit-aware frequency distribution of induced and non-induced quads, i.e. subgraphs of size four. Given an edge matrix, data frame, or a graph object (e.g., igraph'), the orbit-aware counts are computed respective each of the edges and nodes.

This package provides a wrapper around the generic coordinate transformation software PROJ that transforms coordinates from one coordinate reference system ('CRS') to another. This includes cartographic projections as well as geodetic transformations. The intention is for this package to be used by user-packages such as reproj', and that the older PROJ.4 and version 5 pathways be provided by the proj4 package.

This package provides tools for performing disproportionality analysis using the information component, proportional reporting rate and the reporting odds ratio. The anticipated use is passing data to the da() function, which executes the disproportionality analysis. See Norén et al (2011) <doi:10.1177/0962280211403604> and Montastruc et al (2011) <doi:10.1111/j.1365-2125.2011.04037.x> for further details.

This package provides a tidyverse'-style interface to high-performance vehicle routing problem (VRP) solving. Vendors the C++ core of the PyVRP solver (<https://github.com/PyVRP/PyVRP>) and rewires it through cpp11', with no Python runtime dependency. Supports the capacitated VRP, time windows, multiple depots, heterogeneous fleets, prize-collecting and multi-trip variants, driven by an iterated local search metaheuristic.

Converts double-precision floating-point values to decimal strings with the shortest significands needed for round-trip recovery. Uses the zmij algorithm through a vendored Rust implementation. The method is described in Zverovich (2025), "Faster Double-to-String Conversion" <https://vitaut.net/posts/2025/faster-dtoa/>. Provides vectorized formatting, a correctly rounded inverse parser, and explicit handling of missing and non-finite values.

Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. This R package provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions and hierarchies, and facilities for computing on them, including methods for measuring proximity and obtaining consensus and secondary clusterings.

This package provides tooling to group dates by a variety of periods including: yearly, monthly, by second, by week of the month, and more. The groups are defined in such a way that they also represent the distance between dates in terms of the period. This extracts valuable information that can be used in further calculations that rely on a specific temporal spacing between observations.

Converts microscopy video and image data (H.264/H.265, AVI/MJPEG, TIFF stacks) to the AV1 codec for compact storage, and archives 16-bit image stacks and videos losslessly in the FFV1 format. Encoding runs on the GPU via Vulkan or VAAPI when available and falls back to the CPU through FFmpeg and SVT-AV1'. Whole experiment folders can be converted at once.

Fits a Classifier Chain Network (CCN) for multi-label binary classification. The model jointly learns label dependencies through a chain structure and optimizes an Lq norm aggregated cross-entropy loss. Provides methods for prediction, model summary, and k-fold cross-validation with grid search over hyperparameters. The methodology is described in Touw and Van de Velden (2025) <doi:10.1016/j.eswa.2025.128048>.

Duplicated publication data (pre-processed and formatted) for entity resolution. This data set contains a total of 1879 records. The following variables are included in the data set: id, title, book title, authors, address, date, year, editor, journal, volume, pages, publisher, institution, type, tech, note. The data set has a respective gold data set that provides information on which records match based on id.

Google's Compact Language Detector 3 is a neural network model for language identification and the successor of cld2 (available from CRAN). The algorithm is still experimental and takes a novel approach to language detection with different properties and outcomes. It can be useful to combine this with the Bayesian classifier results from cld2'. See <https://github.com/google/cld3#readme> for more information.

Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

This package provides a robust identification of differential binding sites method for analyzing ChIP-seq (Chromatin Immunoprecipitation Sequencing) comparing two samples that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) allowing for flexible modeling of data. Methods for Differential Identification using Mixture Ensemble (DIME) is described in: Taslim et al., (2011) <doi:10.1093/bioinformatics/btr165>.

Set of functions for Data Envelopment Analysis, including classical, fuzzy, cross-efficiency, bootstrapping, and Malmquist models. See: Banker, R.; Charnes, A.; Cooper, W.W. (1984). <doi:10.1287/mnsc.30.9.1078>, Charnes, A.; Cooper, W.W.; Rhodes, E. (1978). <doi:10.1016/0377-2217(78)90138-8> and Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). <doi:10.1287/mnsc.27.6.668>.

The goal of dndR is to provide a suite of Dungeons & Dragons related functions. This package is meant to be useful both to players and Dungeon Masters (DMs). Some functions apply to many tabletop role-playing games (e.g., dice rolling), but others are focused on Fifth Edition (a.k.a. "5e") and where possible both the 2014 and 2024 versions are supported.

Regular and non-regular Fractional Factorial 2-level designs can be created. Furthermore, analysis tools for Fractional Factorial designs with 2-level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the built-in function alias).

This package provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.

Simulating and estimating (regime-switching) Markov chain Gaussian fields with spatio-temporal covariance functions of the Gneiting class (Gneiting 2002) <doi:10.1198/016214502760047113>, including the regime-switching framework of Jia and Sezer (2025) <doi:10.1063/5.0285012>. It supports parameter estimation by weighted least squares and approximate conditional maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations.

Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). The models can be set up and modified with user-friendly functions and are fit to the data using Stan for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using knitr'. Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.

Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, ART and SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included.