The main functionalities of wrappedtools are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well.

This package provides an R interface to the Whapi API <https://whapi.cloud>, enabling sending and receiving WhatsApp messages directly from R'. Functions include sending text, images, documents, stickers, geographic locations, and interactive messages (buttons and lists). Also includes webhook parsing utilities and channel health checks.

All functions and data sets required for the examples in the book Hyndman (2026) "That's Weird: Anomaly Detection Using R" <https://OTexts.com/weird/>. All packages needed to run the examples are also loaded.

This package provides a client for the WebDriver API'. It allows driving a (probably headless) web browser, and can be used to test web applications, including Shiny apps. In theory it works with any WebDriver implementation, but it was only tested with PhantomJS'.

This package provides a collection of implementations of classical and novel algorithms for weighted sampling without replacement. Implementations include the algorithm of Efraimidis and Spirakis (2006) <doi:10.1016/j.ipl.2005.11.003> and Wong and Easton (1980) <doi:10.1137/0209009>.

Makes available code necessary to reproduce figures and tables in papers on the WaveD method for wavelet deconvolution of noisy signals as presented in The WaveD Transform in R, Journal of Statistical Software Volume 21, No. 3, 2007.

This package implements various win ratio methodologies for composite endpoints of death and non-fatal events, including the (stratified) proportional win-fractions (PW) regression models (Mao and Wang, 2020 <doi:10.1111/biom.13382>), (stratified) two-sample tests with possibly recurrent nonfatal event, and sample size calculation for standard win ratio test (Mao et al., 2021 <doi:10.1111/biom.13501>).

This package provides a WebSocket client interface for R. WebSocket is a protocol for low-overhead real-time communication: <https://en.wikipedia.org/wiki/WebSocket>.

Adds ... to a function's argument list so that it can tolerate non-matching arguments.

This method generates a tour path by interpolating between d-D frames in p-D using Givens rotations. The algorithm arises from the problem of zeroing elements of a matrix. This interpolation method is useful for showing specific d-D frames in the tour, as opposed to d-D planes, as done by the geodesic interpolation. It is useful for projection pursuit indexes which are not s invariant. See more details in Buj, Cook, Asimov and Hurley (2005) <doi:10.1016/S0169-7161(04)24014-7> and Batsaikhan, Cook and Laa (2023) <doi:10.48550/arXiv.2311.08181>.

This package implements a functional approximation of the four panel cointegration tests developed by Westerlund (2007) <doi:10.1111/j.1468-0084.2007.00477.x>. The tests are based on structural rather than residual dynamics and allow for heterogeneity in both the long-run cointegrating relationship and the short-run dynamics. The package includes logic for automated lag and lead selection via AIC/BIC, Bartlett kernel long-run variance estimation, and a bootstrap procedure to handle cross-sectional dependence. It also includes a bootstrapping distribution visualization function for diagnostic purposes.

Builds a joint probabilistic forecast across series and horizons using adaptive copulas (Gaussian/t) with shrinkage-repaired correlations. At the low level it calls a probabilistic mixer per series and horizon, which backtests several simple predictors, predicts next-window Continuous Ranked Probability Score (CRPS), and converts those scores into softmax weights to form a calibrated mixture (r/q/p/dfun). The mixer blends eight simple predictors: a naive predictor that wraps the last move in a PERT distribution; an arima predictor using auto.arima for one-step forecasts; an Exponentially Weighted Moving Average (EWMA) gaussian predictor with mean/variance under a Gaussian; a historical bootstrap predictor that resamples past horizon-aligned moves; a drift residual bootstrap predictor combining linear trend with bootstrapped residuals; a volatility-scaled naive predictor centering on the last move and scaling by recent volatility; a robust median mad predictor using median/MAD with Laplace or Normal shape; and a shrunk quantile predictor that fits a few quantile regressions over time and interpolates to a full predictive. The function then couples the per-series mixtures on a common transform (additive/multiplicative/log-multiplicative), simulates coherent draws, and returns both transformed- and level-scale samplers and summaries.

Estimates high-dimensional multivariate normal copula regression models with the weighted composite likelihood estimating equations in Nikoloulopoulos (2023) <doi:10.1016/j.csda.2022.107654>. It provides autoregressive moving average correlation structures and binary, ordinal, Poisson, and negative binomial regressions.

This package provides a collection of white noise hypothesis tests for functional time series and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models. These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019) <doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, and Kim et al. (2023) <doi: 10.1214/23-SS143> respectively.

This package provides a powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The WrightMap package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. TAM', mirt', eRm or IRToys in R', or Stata', Mplus', etc.), WrightMap features special integration with ConQuest to facilitate reading and plotting its output directly.The wrightMap function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The CQmodel function reads output files created using ConQuest software and creates a set of data frames for easy data manipulation, bundled in a CQmodel object. The wrightMap function can take a CQmodel object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.

This package provides a weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc. Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation. Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. <https://ui.adsabs.harvard.edu/abs/2019AGUFMGC41G1267G>.

Shows the relationship between an independent and dependent variable through Weight of Evidence and Information Value.

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

Treemaps are a visually appealing graphical representation of numerical data using a space-filling approach. A plane or map is subdivided into smaller areas called cells. The cells in the map are scaled according to an underlying metric which allows to grasp the hierarchical organization and relative importance of many objects at once. This package contains two different implementations of treemaps, Voronoi treemaps and Sunburst treemaps. The Voronoi treemap function subdivides the plot area in polygonal cells according to the highest hierarchical level, then continues to subdivide those parental cells on the next lower hierarchical level, and so on. The Sunburst treemap is a computationally less demanding treemap that does not require iterative refinement, but simply generates circle sectors that are sized according to predefined weights. The Voronoi tesselation is based on functions from Paul Murrell (2012) <https://www.stat.auckland.ac.nz/~paul/Reports/VoronoiTreemap/voronoiTreeMap.html>.

Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators ('WDI'), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators.

This package provides two functions frameableWidget()', and frameWidget()'. The frameableWidget() is used to add extra code to a htmlwidget which allows is to be rendered correctly inside a responsive iframe'. The frameWidget() is a htmlwidget which displays content of another htmlwidget inside a responsive iframe'. These functions allow for easier embedding of htmlwidgets in content management systems such as wordpress', blogger etc. They also allow for separation of widget content from main HTML content where CSS of the main HTML could interfere with the widget.

The wavelet-based variance transformation method is used for system modelling and prediction. It refines predictor spectral representation using Wavelet Theory, which leads to improved model specifications and prediction accuracy. Details of methodologies used in the package can be found in Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962>, Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>, and Jiang, Z., Sharma, A., & Johnson, F. (2021) <doi:10.1016/J.JHYDROL.2021.126816>.

Numerous time series admit autoregressive moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent. These models are called weak ARMA by opposition to the standard ARMA models, also called strong ARMA models, in which the error terms are supposed to be independent and identically distributed (iid). This package allows the study of nonlinear time series models through weak ARMA representations. It determines identification, estimation and validation for ARMA models and for AR and MA models in particular. Functions can also be used in the strong case. This package also works on white noises by omitting arguments p', q', ar and ma'. See Francq, C. and Zakoïan, J. (1998) <doi:10.1016/S0378-3758(97)00139-0> and Boubacar Maïnassara, Y. and Saussereau, B. (2018) <doi:10.1080/01621459.2017.1380030> for more details.

Encapsulates the pattern of untidying data into a wide matrix, performing some processing, then turning it back into a tidy form. This is useful for several operations such as co-occurrence counts, correlations, or clustering that are mathematically convenient on wide matrices.