This package implements inferential and graphic procedures for the semiparametric proportional means regression of weighted composite endpoint of recurrent event and death (Mao and Lin, 2016, <doi:10.1093/biostatistics/kxv050>).

Allow users to obtain clean and tidy football (soccer) game, team and player data. Data is collected from a number of popular sites, including FBref', transfer and valuations data from Transfermarkt'<https://www.transfermarkt.com/> and shooting location and other match stats data from Understat'<https://understat.com/> and fotmob'<https://www.fotmob.com/>. It gives users the ability to access data more efficiently, rather than having to export data tables to files before being able to complete their analysis.

This package implements Weighted-Average Least Squares model averaging for negative binomial regression models of Huynh (2024) <doi:10.48550/arXiv.2404.11324>, generalized linear models of De Luca, Magnus, Peracchi (2018) <doi:10.1016/j.jeconom.2017.12.007> and linear regression models of Magnus, Powell, Pruefer (2010) <doi:10.1016/j.jeconom.2009.07.004>, see also Magnus, De Luca (2016) <doi:10.1111/joes.12094>. Weighted-Average Least Squares for the linear regression model is based on the original MATLAB code by Magnus and De Luca <https://www.janmagnus.nl/items/WALS.pdf>, see also Kumar, Magnus (2013) <doi:10.1007/s13571-013-0060-9> and De Luca, Magnus (2011) <doi:10.1177/1536867X1201100402>.

Easily plot heat maps of the world, based on continuous or categorical data. Country labels can also be added to the map.

This package provides data to be used by the wordpiece algorithm in order to tokenize text into somewhat meaningful chunks. Included vocabularies were retrieved from <https://huggingface.co/bert-base-cased/resolve/main/vocab.txt> and <https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt> and parsed into an R-friendly format.

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'.

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 function, wget_set(), to change the method (default to wget -c') using in download.file(). Using wget -c allowing continued downloading, which is especially useful for slow internet connection and for downloading large files. User can run wget_unset() to restore previous setting.

Speech-to-text transcription using a native R torch implementation of OpenAI Whisper model <https://github.com/openai/whisper>. Supports multiple model sizes from tiny (39M parameters) to large-v3 (1.5B parameters) with integrated download from HuggingFace <https://huggingface.co/> via the hfhub package. Provides automatic speech recognition with optional language detection and translation to English. Audio preprocessing, mel spectrogram computation, and transformer-based encoder-decoder inference are all implemented in R using the torch package.

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.

Interface to the World Inequality Database (WID) API <https://wid.world>. Downloads distributional national accounts data with filters for country, year, percentile, age group, and population type. Includes code validation and reference tables. Independent implementation unaffiliated with the World Inequality Lab (WIL) or the Paris School of Economics.

Computation of approximate potentials for both gradient and non gradient fields. It is known from physics that only gradient fields, also known as conservative, have a well defined potential function. Here we present an algorithm, based on the classical Helmholtz decomposition, to obtain an approximate potential function for non gradient fields. More information in Rodrà guez-Sánchez (2020) <doi:10.1371/journal.pcbi.1007788>.

Descriptive statistics for large data tend to be low resolution on the tails. Whisker Odds generate a table of descriptive statistics for large data. This is the same as letter-values, but with an alternative naming of depths which allow for depths beyond 26. For a reference to letter-values see Heike Hofmann and Hadley Wickham and Karen Kafadar (2017) <doi:10.1080/10618600.2017.1305277>.

Retrieve geographical information for airports using their IATA or ICAO codes.

First, we provide functions to calculate the partial derivative of the first-passage time diffusion probability density function (PDF) and cumulative distribution function (CDF) with respect to the first-passage time t (only for PDF), the upper barrier a, the drift rate v, the relative starting point w, the non-decision time t0, the inter-trial variability of the drift rate sv, the inter-trial variability of the rel. starting point sw, and the inter-trial variability of the non-decision time st0. In addition the PDF and CDF themselves are also provided. Most calculations are done on the logarithmic scale to make it more stable. Since the PDF, CDF, and their derivatives are represented as infinite series, we give the user the option to control the approximation errors with the argument precision'. For the numerical integration we used the C library cubature by Johnson, S. G. (2005-2013) <https://github.com/stevengj/cubature>. Numerical integration is required whenever sv, sw, and/or st0 is not zero. Note that numerical integration reduces speed of the computation and the precision cannot be guaranteed anymore. Therefore, whenever numerical integration is used an estimate of the approximation error is provided in the output list. Note: The large number of contributors (ctb) is due to copying a lot of C/C++ code chunks from the GNU Scientific Library (GSL). Second, we provide methods to sample from the first-passage time distribution with or without user-defined truncation from above. The first method is a new adaptive rejection sampler building on the works of Gilks and Wild (1992; <doi:10.2307/2347565>) and Hartmann and Klauer (in press). The second method is a rejection sampler provided by Drugowitsch (2016; <doi:10.1038/srep20490>). The third method is an inverse transformation sampler. The fourth method is a "pseudo" adaptive rejection sampler that builds on the first method. For more details see the corresponding help files.

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>.

Computes inequality measures of a given variable taking into account weights. Suitable for ratio, interval and ordered scale. Includes Gini, Theil, Leti index, Palma ratio, 20:20 ratio, Allison and Foster index, Jenkins index, Cowell and Flechaire index, Abul Naga and Yalcin index, Apouey index, Blair and Lacy index. Bootstrap provides distribution of inequality measures enabling significance tests.

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 functions for subject/instance weighted support vector machines (SVM). It uses a modified version of libsvm and is compatible with package e1071'. It also allows user defined kernel matrix.

Imports variables from ReaderBench (Dascalu et al., 2018)<doi:10.1007/978-3-319-66610-5_48>, Coh-Metrix (McNamara et al., 2014)<doi:10.1017/CBO9780511894664>, and/or GAMET (Crossley et al., 2019) <doi:10.17239/jowr-2019.11.02.01> output files; downloads predictive scoring models described in Mercer & Cannon (2022)<doi:10.31244/jero.2022.01.03> and Mercer et al.(2021)<doi:10.1177/0829573520987753>; and generates predicted writing quality and curriculum-based measurement (McMaster & Espin, 2007)<doi:10.1177/00224669070410020301> scores.

This package provides a comprehensive suite of functions for processing, analyzing, and visualizing textual data from tweets is offered. Users can clean tweets, analyze their sentiments, visualize data, and examine the correlation between sentiments and environmental data such as weather conditions. Main features include text processing, sentiment analysis, data visualization, correlation analysis, and synthetic data generation. Text processing involves cleaning and preparing tweets by removing textual noise and irrelevant words. Sentiment analysis extracts and accurately analyzes sentiments from tweet texts using advanced algorithms. Data visualization creates various charts like word clouds and sentiment polarity graphs for visual representation of data. Correlation analysis examines and calculates the correlation between tweet sentiments and environmental variables such as weather conditions. Additionally, random tweets can be generated for testing and evaluating the performance of analyses, empowering users to effectively analyze and interpret Twitter data for research and commercial purposes.

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

Fits the combination of Wavelet-GARCH model for time series forecasting using algorithm by Paul (2015) <doi:10.3233/MAS-150328>.

The wavelet and ANN technique have been combined to reduce the effect of data noise. This wavelet-ANN conjunction model is able to forecast time series data with better accuracy than the traditional time series model. This package fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI: 10.1007/s00521-017-3289-9>.