Analyzes and models data subject to sampling biases. Provides functions to estimate the density and cumulative distribution functions from biased samples of continuous distributions. Includes the estimators proposed by Bhattacharyya et al. (1988) <doi:10.1080/03610928808829825> and Jones (1991) <doi:10.2307/2337020> for density, and by Cox (2005, ISBN:052184939X) and Bose and Dutta (2022) <doi:10.1007/s00184-021-00824-3> for distribution, with different bandwidth selectors. Also includes a real length-biased dataset on shrub width from Muttlak (1988) <https://www.proquest.com/openview/3dd74592e623cdbcfa6176e85bd3d390/1?cbl=18750&diss=y&pq-origsite=gscholar>.

This package performs Wilcoxon-Mann-Whitney test in the presence of missing data with controlled Type I error regardless of the values of missing data.

Urban water and sanitation survey dataset collected by Water and Sanitation for the Urban Poor (WSUP) with technical support from Valid International. These citywide surveys have been collecting data allowing water and sanitation service levels across the entire city to be characterised, while also allowing more detailed data to be collected in areas of the city of particular interest. These surveys are intended to generate useful information for others working in the water and sanitation sector. Current release version includes datasets collected from a survey conducted in Dhaka, Bangladesh in March 2017. This survey in Dhaka is one of a series of surveys to be conducted by WSUP in various cities in which they operate including Accra, Ghana; Nakuru, Kenya; Antananarivo, Madagascar; Maputo, Mozambique; and, Lusaka, Zambia. This package will be updated once the surveys in other cities are completed and datasets have been made available.

This package provides tools for a wavelet-based approach to analyzing spatial synchrony, principally in ecological data. Some tools will be useful for studying community synchrony. See, for instance, Sheppard et al (2016) <doi: 10.1038/NCLIMATE2991>, Sheppard et al (2017) <doi: 10.1051/epjnbp/2017000>, Sheppard et al (2019) <doi: 10.1371/journal.pcbi.1006744>.

This package implements weighted double score matching (WDSM) for estimating population-level causal effects from complex survey data. Combines propensity scores and prognostic scores with survey design weights for matching, survey-weighted imputation within match sets, and Hajek normalization to target the population average treatment effect (PATE) and the population average treatment effect on the treated (PATT). Supports both retrospective (treatment-dependent) and prospective (treatment-independent) sampling designs. Achieves double robustness: consistent estimation when either the propensity score or prognostic score model is correctly specified. Provides polynomial sieve bias correction and linearization-based multinomial bootstrap variance estimation that preserves the survey-weighted matching structure without re-matching. Methods are described in Zeng, Tong, Tong, Lu, Mukherjee, and Li (2026, under review) "Where to weight? Estimating population causal effects with weighted double score matching in complex surveys".

Efficiently read and write Waveform (WAV) audio files <https://en.wikipedia.org/wiki/WAV>. Support for unsigned 8 bit Pulse-code modulation (PCM), signed 12, 16, 24 and 32 bit PCM and other encodings.

This package provides estimation and inference for while-alive regression models targeting the while-alive loss rate for composite endpoints that include recurrent events and a terminal event. The implementation supports flexible time-varying covariate effects through user-selected time bases, including B-splines, natural splines, M-splines, step functions, truncated linear bases, interval-local bases, and piecewise polynomials. Inference can be performed using cluster-robust variance estimators for cluster-randomized trials, with subject-level (IID) variance as a special case. The package includes prediction and plotting utilities and K-fold cross-validation for selecting basis and tuning parameters. Methodology is based on Fang et al. (2025) <doi:10.1093/biostatistics/kxaf047>.

Create dense vector representation of words and documents using quanteda'. Implements Word2vec (Mikolov et al., 2013) <doi:10.48550/arXiv.1310.4546>, Doc2vec (Le & Mikolov, 2014) <doi:10.48550/arXiv.1405.4053> and Latent Semantic Analysis (Deerwester et al., 1990) <doi:10.1002/(SICI)1097-4571(199009)41:6%3C391::AID-ASI1%3E3.0.CO;2-9>.

Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test that is needed for a given power and two sided type I error rate. The method works for metric data with and without ties, count data, ordered categorical data, and even dichotomous data. But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect. See Happ et al. (2019, <doi:10.1002/sim.7983>) for details.

Logging of scripts suitable for clinical trials using Quarto to create nice human readable logs. whirl enables execution of scripts in batch, while simultaneously creating logs for the execution of each script, and providing an overview summary log of the entire batch execution.

Heuristic methods to solve the routing problems in a warehouse management. Package includes several heuristics such as the Midpoint, Return, S-Shape and Semi-Optimal Heuristics for designation of the pickerâ s route in order picking. The heuristics aim to provide the acceptable travel distances while considering warehouse layout constraints such as aisles and shelves. It also includes implementation of the COPRAS (COmplex PRoportional ASsessment) method for supporting selection of locations to be visited by the picker in shared storage systems. The package is designed to facilitate more efficient warehouse routing and logistics operations. see: Bartholdi, J. J., Hackman, S. T. (2019). "WAREHOUSE & DISTRIBUTION SCIENCE. Release 0.98.1." The Supply Chain & Logistics Institute. H. Milton Stewart School of Industrial and Systems Engineering. Georgia Institute of Technology. <https://www.warehouse-science.com/book/editions/wh-sci-0.98.1.pdf>.

Helper functions to easily add functionality to functions. The package can assign functions to have an lazy evaluation allowing you to save and update the arguments before and after each function call. You can set a temporary working directory within functions and wrap console messages around other functions.

Converts pathways from WikiPathways GPML format or KEGG KGML format into igraph objects. Includes tools to find all cycles in the resulting graphs and determine which ones involve negative feedback (inhibition).

This package provides a new inverse probability of selection weighted Cox model to deal with outcome-dependent sampling in survival analysis.

This package provides functions aiming to facilitate the analysis of the structure of animal acoustic signals in R'. warbleR makes use of the basic sound analysis tools from the packages tuneR and seewave', and offers new tools for exploring and quantifying acoustic signal structure. The package allows to organize and manipulate multiple sound files, create spectrograms of complete recordings or individual signals in different formats, run several measures of acoustic structure, and characterize different structural levels in acoustic signals (Araya-Salas et al 2016 <doi:10.1111/2041-210X.12624>).

This package provides a fast visualization tool for creating wordcloud by using wordcloud2.js'. wordcloud2.js is a JavaScript library to create wordle presentation on 2D canvas or HTML <https://timdream.org/wordcloud2.js/>.

Use the what3words API <https://developer.what3words.com/public-api> to return three words which uniquely identify every 3m x 3m square on Earth. It is also possible to return coordinates from any valid three words location. Supports multiple languages.

An R frontend for the WhiteboxTools library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. WhiteboxTools can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. WhiteboxTools also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) <doi:10.1016/j.cageo.2016.07.003>.

Toolkit to support and perform discrete event simulations with and without resource constraints in the context of health technology assessments (HTA). The package focuses on cost-effectiveness modelling and aims to be submission-ready to relevant HTA bodies in alignment with NICE TSD 15 <https://sheffield.ac.uk/nice-dsu/tsds/patient-level-simulation>. More details an examples can be found in the package website <https://jsanchezalv.github.io/WARDEN/>.

This package provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.

Within-subject mediation analysis using structural equation modeling. Examine how changes in an outcome variable between two conditions are mediated through one or more variables. Supports within-subject mediation analysis using the lavaan package by Rosseel (2012) <doi:10.18637/jss.v048.i02>, and extends Monte Carlo confidence interval estimation to missing data scenarios using the semmcci package by Pesigan and Cheung (2023) <doi:10.3758/s13428-023-02114-4>.

Displays geospatial data on an interactive 3D globe in the web browser.

This package provides a toolkit to detect clusters from distance matrices. The distance matrices are assumed to be calculated between the cells of multiple animals ('Caenorhabditis elegans') from input time-series matrices. Some functions for generating distance matrices, performing clustering, evaluating the clustering, and visualizing the results of clustering and evaluation are available. We're also providing the download function to retrieve the calculated distance matrices from figshare <https://figshare.com>.

In the course of a genome-wide association study, the situation often arises that some phenotypes are known with greater precision than others. It could be that some individuals are known to harbor more micro-environmental variance than others. In the case of inbred strains of model organisms, it could be the case that more organisms were observed from some strains than others, so the strains with more organisms have better-estimated means. Package wISAM handles this situation by allowing for weighting of each observation according to residual variance. Specifically, the weight parameter to the function conduct_scan() takes the precision of each observation (one over the variance).