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 a suite of routines for Weyl algebras. Notation follows Coutinho (1995, ISBN 0-521-55119-6, "A Primer of Algebraic D-Modules"). Uses disordR discipline (Hankin 2022 <doi:10.48550/arXiv.2210.03856>). To cite the package in publications, use Hankin 2022 <doi:10.48550/arXiv.2212.09230>.

Implementation of the methodologies described in 1) Alexander Petersen, Xi Liu and Afshin A. Divani (2021) <doi:10.1214/20-aos1971>, including global F tests, partial F tests, intrinsic Wasserstein-infinity bands and Wasserstein density bands, and 2) Chao Zhang, Piotr Kokoszka and Alexander Petersen (2022) <doi:10.1111/jtsa.12590>, including estimation, prediction, and inference of the Wasserstein autoregressive models.

This package provides efficient implementation of the Wild Binary Segmentation and Binary Segmentation algorithms for estimation of the number and locations of multiple change-points in the piecewise constant function plus Gaussian noise model.

Create, store, read and manage structured collections of datasets and other objects using a workspace', then bundle it into a compressed archive. Using open and interoperable formats makes it possible to exchange bundled data from R to other languages such as Python or Julia'. Multiple formats are supported Parquet', JSON', yaml', spatial data and raster data are supported.

This package provides a utility for working with women's basketball data. A scraping and aggregating interface for the WNBA Stats API <https://stats.wnba.com/> and ESPN's <https://www.espn.com> women's college basketball and WNBA statistics. It provides users with the capability to access the game play-by-plays, box scores, standings and results to analyze the data for themselves.

This package provides a comprehensive data analysis framework for NIH-funded research that streamlines workflows for both data cleaning and preparing NIH Data Archive ('NDA') submission templates. Provides unified access to multiple data sources ('REDCap', MongoDB', Qualtrics', SQL', ORACLE') through interfaces to their APIs, with specialized functions for data cleaning, filtering, merging, and parsing. Features automatic validation, field harmonization, and memory-aware processing to enhance reproducibility in multi-site collaborative research as described in Mittal et al. (2021) <doi:10.20900/jpbs.20210011>.

This package provides a comprehensive toolbox for wavelet-domain quantile analyses of bivariate and multivariate time series. Provides Wavelet Quantile Regression and Multivariate Wavelet Quantile Regression after Adebayo and Ozkan (2024) <doi:10.1016/j.jclepro.2024.140832>, Wavelet Quantile-on-Quantile regression with bootstrap p-values extending Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>, the nonparametric Causality-in-Quantiles test of Balcilar, Gupta and Pierdzioch (2016) <doi:10.1016/j.resourpol.2016.04.004> together with its wavelet variant, Wavelet Quantile Mediation and Moderation, Wavelet Quantile Correlation, and a wavelet-based nonparametric Quantile Density estimator. The Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition is performed via waveslim and Short / Medium / Long band aggregation is supported throughout. For plain Quantile-on-Quantile regression see the companion CRAN package QuantileOnQuantile'. All interactive 3D surfaces, heatmaps and contour plots default to the MATLAB Parula colour map.

Calculate magnetic field at a given location and time according to the World Magnetic Model (WMM). Both the main field and secular variation components are returned. This functionality is useful for physicists and geophysicists who need orthogonal components from WMM. Currently, this package supports annualized time inputs between 2000 and 2025. If desired, users can specify which WMM version to use, e.g., the original WMM2015 release or the recent out-of-cycle WMM2015 release. Methods used to implement WMM, including the Gauss coefficients for each release, are described in the following publications: Chulliat et al (2020) <doi:10.25923/ytk1-yx35>, Chulliat et al (2019) <doi:10.25921/xhr3-0t19>, Chulliat et al (2015) <doi:10.7289/V5TB14V7>, Maus et al (2010) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/WMM2010_Report.pdf>, McLean et al (2004) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/TRWMM_2005.pdf>, and Macmillian et al (2000) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/wmm2000.pdf>.

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, and Hoeffding's D; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0> and Hollander et al. (2015, ISBN:9780470387375).

An interface to WordNet using the Jawbone Java API to WordNet. WordNet (<https://wordnet.princeton.edu/>) is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. Please note that WordNet(R) is a registered tradename. Princeton University makes WordNet available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>). The WordNet database files need to be made available separately, either via package wordnetDicts from <https://datacube.wu.ac.at>, installing system packages where available, or direct download from <https://wordnetcode.princeton.edu/3.0/WNdb-3.0.tar.gz>.

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

Weighted versions of common descriptive statistics (variance, standard deviation, covariance, correlation, quantiles).

High-level tools to attach gridded weather data from the NASA POWER Project to event-based datasets. The package plans efficient spatio-temporal API calls via the nasapower R package, caches downloaded segments locally, and joins weather variables back to the input table using exact or rolling joins. This package is not affiliated with or endorsed by NASA.

This package provides tools for extracting and analyzing cyclic signals from time series.

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 a clean syntax for vectorising the use of Non-Standard Evaluation (NSE), for example in ggplot2', dplyr', or data.table'.

Lossless webp images are 26% smaller in size compared to PNG. Lossy webp images are 25-34% smaller in size compared to JPEG. This package reads and writes webp images into a 3 (rgb) or 4 (rgba) channel bitmap array using conventions from the jpeg and png packages.

Top-Down mass spectrometry aims to identify entire proteins as well as their (post-translational) modifications or ions bound (eg Chen et al (2018) <doi:10.1021/acs.analchem.7b04747>). The pattern of internal fragments (Haverland et al (2017) <doi:10.1007/s13361-017-1635-x>) may reveal important information about the original structure of the proteins studied (Skinner et al (2018) <doi:10.1038/nchembio.2515> and Li et al (2018) <doi:10.1038/nchem.2908>). However, the number of possible internal fragments gets huge with longer proteins and subsequent identification of internal fragments remains challenging, in particular since the the accuracy of measurements with current mass spectrometers represents a limiting factor. This package attempts to deal with the complexity of internal fragments and allows identification of terminal and internal fragments from deconvoluted mass-spectrometry data.

This package provides a workflow for your analysis projects by combining literate programming ('knitr and rmarkdown') and version control ('Git', via git2r') to generate a website containing time-stamped, versioned, and documented results.

ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.

Inferences about counterfactuals are essential for prediction, answering what if questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Unfortunately, standard statistical approaches assume the veracity of the model rather than revealing the degree of model-dependence, which makes this problem hard to detect. WhatIf offers easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. If an analysis fails the tests offered here, then we know that substantive inferences will be sensitive to at least some modeling choices that are not based on empirical evidence, no matter what method of inference one chooses to use. WhatIf implements the methods for evaluating counterfactuals discussed in Gary King and Langche Zeng, 2006, "The Dangers of Extreme Counterfactuals," Political Analysis 14 (2) <DOI:10.1093/pan/mpj004>; and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The Pitfalls of Counterfactual Inference," International Studies Quarterly 51 (March) <DOI:10.1111/j.1468-2478.2007.00445.x>.

Calculate the win statistics (win ratio, net benefit and win odds) for prioritized multiple endpoints, plot the win statistics and win proportions over study time if at least one time-to-event endpoint is analyzed, and simulate datasets with dependent endpoints. The package can handle any type of outcomes (continuous, ordinal, binary, time-to-event) and allow users to perform stratified analysis, inverse probability of censoring weighting (IPCW) and inverse probability of treatment weighting (IPTW) analysis.