This package provides the functions to perform a Welch's one-way Anova with fixed effects based on summary statistics (sample size, means, standard deviation) and the Games-Howell post hoc test for multiple comparisons and provides the effect size estimator adjusted omega squared. In addition sample size estimation can be computed based on Levy's method, and a Monte Carlo simulation is included to bootstrap residual normality and homoscedasticity Welch, B. L. (1951) <doi:10.1093/biomet/38.3-4.330> Kirk, R. E. (1996) <doi:10.1177/0013164496056005002> Carroll, R. M., & Nordholm, L. A. (1975) <doi:10.1177/001316447503500304> Albers, C., & Lakens, D. (2018) <doi:10.1016/j.jesp.2017.09.004> Games, P. A., & Howell, J. F. (1976) <doi:10.2307/1164979> Levy, K. J. (1978a) <doi:10.1080/00949657808810246> Show-Li, J., & Gwowen, S. (2014) <doi:10.1111/bmsp.12006>.

Organizational framework for web development in R including functions to serve static and dynamic content via HTTP methods, includes the html5 package to create HTML pages, and offers other utility functions for common tasks related to web development.

This package provides a collection of color palettes that were extracted from various books on my sons(Wren) bookshelf. Also included are a number of functions and wrappers to utilize them, as well as to subset the palettes to desired number/specific colors.

This package provides maximum likelihood estimation methods for eight modified Weibull-type distributions. It returns parameter estimates, log-likelihood, AIC, and BIC, and also supports model fitting, validation, and comparison across different distributional forms. These methods can be applied to reliability, survival, and lifetime data analysis, making the package useful for researchers and practitioners in statistics, engineering, and medicine. The following distributions are included: Rangoli2023, Peng2014, Lai2003, Xie1996, Sarhan2009, Rangoli2025, Mustafa2012, and Alwasel2009.

Query Wikidata and get facts from current and historic Wikipedia main pages.

Apply Wordpiece (<arXiv:1609.08144>) tokenization to input text, given an appropriate vocabulary. The BERT (<arXiv:1810.04805>) tokenization conventions are used by default.

Establishes and maintains vertex pool topology for geometry handled by wk'. Segments are the atomic unit, vertices are shared via integer references into a pool. Topology is made discoverable via coincident vertex detection while not requiring modification of the input data. Topological data models follow principles described in Worboys and Duckham (2004, ISBN:978-0415283755). The edge-based topology geometry decomposed into vertices and directed edge pairs is a simplification of the quad-edge case in Guibas & Stolfi (1985) <doi:10.1145/282918.282923>.

This package provides efficient implementations of weighted dependence measures and related asymptotic tests for independence. Implemented measures are the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, Hoeffding's D, and Chatterjee's xi; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0>, Hollander et al. (2015, ISBN:9780470387375), and Chatterjee (2021) <doi:10.1080/01621459.2020.1758115>.

This package provides insight into how the best hand for a poker game changes based on the game dealt, players who stay in until the showdown and wildcards added to the base game. At this time the package does not support player tactics, so draw poker variants are not included.

This package provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <doi:10.48550/arXiv.1607.05858>.

Bayesian wavelet denoising using a resolution-adaptive spike-and-slab prior whose continuous slab is a mixture of a compactly supported Wendland-type density and the semicircle density. The package provides Gaussian and Laplace working-likelihood versions, empirical-Bayes fitting of resolution-specific slab weights, robust noise-scale estimation, posterior-mean coefficient shrinkage, reconstruction by the inverse discrete wavelet transform, and diagnostic tools.

Assortativity coefficients, centrality measures, and clustering coefficients for weighted and directed networks. Rewiring unweighted networks with given assortativity coefficients. Generating general preferential attachment networks.

This package provides a workflow for exploring World Development Indicators (WDI) country-level panel data. It downloads WDI data using the WDI package and computes diagnostic indices that capture the temporal behaviour of the data by incorporating the grouping structure of the data. The set of diagnostic indices implemented includes variation features, trend and shape features, and sequential temporal features. This method is described in Akinfenwa, Cahill, and Hurley (2025) "'wdiexplorer': An R package Designed for Exploratory Analysis of World Development Indicators (WDI) Data" <doi:10.48550/arXiv.2511.07027>. We adapt the clustering diagnostics and visualisation methodology described in Rousseeuw (1987) <doi:10.1016/0377-0427(87)90125-7> and selected time series features from Hyndman and Athanasopoulos (2021) "Forecasting: Principles and Practice" <https://otexts.com/fpp3/>.

Download and search data from the World Bank APIs, including the Indicators API, the Poverty and Inequality Platform (PIP) API, the Finances One API, and the Projects API. See <https://datahelpdesk.worldbank.org/knowledgebase/articles/889386-developer-information-overview> for further details.

Estimation, simulation, hypothesis testing, AR-order selection, and forecasting for univariate higher-order stochastic volatility SV(p) models. Supports Gaussian, Student-t, and Generalized Error Distribution (GED) innovations, with optional leverage effects. Estimation uses closed-form Winsorized ARMA-SV (W-ARMA-SV) moment-based methods that avoid numerical optimization. Hypothesis testing includes Local Monte Carlo (LMC) and Maximized Monte Carlo (MMC) procedures for leverage effects, heavy tails, and autoregressive order. AR-order selection is also available via information criteria (BIC/AIC) using the Kalman-filter quasi-likelihood and the Hannan-Rissanen ARMA residual variance. Forecasting is based on Kalman filtering and smoothing. See Ahsan and Dufour (2021) <doi:10.1016/j.jeconom.2021.03.008>, Ahsan, Dufour, and Rodriguez-Rondon (2025) <doi:10.1111/jtsa.12851>, and Ahsan, Dufour, and Rodriguez-Rondon (2026) <doi:10.34989/swp-2026-8> for details.

This package provides methods for estimating profit, profit-maximizing price, demand and consumer surplus of Word-of-Mouth-campaigns on mean-field networks.

This package implements the Welch-Satterthwaite approximation for differences of non-standardized t-distributed random variables in both univariate and multivariate settings. The package provides methods for computing effective degrees of freedom and scale parameters, as well as distribution functions for the approximated difference distribution. The methodology extends the classical Welch-Satterthwaite framework from variance combinations to t-distribution differences through careful moment matching. Methods build on the classical Welch-Satterthwaite approach described in Welch (1947) <doi:10.1093/biomet/34.1-2.28> and Satterthwaite (1946) <doi:10.2307/3002019>.

The wavelet-based quantile mapping (WQM) technique is designed to correct biases in spatio-temporal precipitation forecasts across multiple time scales. The WQM method effectively enhances forecast accuracy by generating an ensemble of precipitation forecasts that account for uncertainties in the prediction process. For a comprehensive overview of the methodologies employed in this package, please refer to Jiang, Z., and Johnson, F. (2023) <doi:10.1029/2022EF003350>. The package relies on two packages for continuous wavelet transforms: WaveletComp', which can be installed automatically, and wmtsa', which is optional and available from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/wmtsa/>. Users need to manually install wmtsa from this archive if they prefer to use wmtsa based decomposition.

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>.

The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.

Supports systematic scrutiny, modification, and integration of data. The function status() counts rows that have missing values in grouping columns (returned by na() ), have non-unique combinations of grouping columns (returned by dup() ), and that are not locally sorted (returned by unsorted() ). Functions enumerate() and itemize() give sorted unique combinations of columns, with or without occurrence counts, respectively. Function ignore() drops columns in x that are present in y, and informative() drops columns in x that are entirely NA; constant() returns values that are constant, given a key. Data that have defined unique combinations of grouping values behave more predictably during merge operations.

An integrated wavelet-based spatial time series modelling framework designed to enhance predictive accuracy under noisy and nonstationary conditions by jointly exploiting multi-resolution (wavelet) information and spatial dependence. The package implements WaveSARIMA() (Wavelet Based Spatial AutoRegressive Integrated Moving Average model using regression features with forecast::auto.arima()) and WaveSNN() (Wavelet Based Spatial Neural Network model using neuralnet with hyperparameter search). Both functions support spatial transformation via a user-supplied spatial matrix, lag feature construction, MODWT-based wavelet sub-series feature generation, time-ordered train/test splitting, and performance evaluation (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE)), returning fitted models and actual vs predicted values for train and test sets. The package has been developed using the algorithm of Paul et al. (2023) <doi:10.1007/s43538-025-00581-1>.

Several analysis-related functions for the book entitled "Web-based Analysis without R in Your Computer"(written in Korean, ISBN 978-89-5566-185-9) by Keon-Woong Moon. The main function plot.htest() shows the distribution of statistic for the object of class htest'.

Computation of the Wasserstein Bipolarization Index as described in Lee and Sobel (Forthcoming) <doi:10.48550/arXiv.2408.03331>. Provides both asymptotic (Sommerfeld, 2017 <https://ediss.uni-goettingen.de/bitstream/handle/11858/00-1735-0000-0023-3FA1-C/DissertationSommerfeldRev.pdf?sequence=1>) and bootstrap methods (Efron and Narasimhan, 2020 <doi:10.1080/10618600.2020.1714633>) for calculating confidence intervals.