This package provides a set of tools for processing and analyzing data developed in the context of the "Who Has Eaten the Planet" (WHEP) project, funded by the European Research Council (ERC). For more details on multi-regional inputâ output model "Food and Agriculture Biomass Inputâ Output" (FABIO) see Bruckner et al. (2019) <doi:10.1021/acs.est.9b03554>.

This package provides tools for fitting and simulating mixtures of Watson distributions. The package is described in Sablica, Hornik and Leydold (2026) <doi:10.18637/jss.v115.i04>. The random sampling scheme of the package offers two sampling algorithms that are based of the results of Sablica, Hornik and Leydold (2022) <doi:10.1080/10618600.2024.2416521>. What is more, the package offers a smart tool to combine these two methods, and based on the selected parameters, it approximates the relative sampling speed for both methods and picks the faster one. In addition, the package offers a fitting function for the mixtures of Watson distribution, that uses the expectation-maximization (EM) algorithm. Special features are the possibility to use multiple variants of the E-step and M-step, sparse matrices for the data representation and state of the art methods for numerical evaluation of needed special functions using the results of Sablica and Hornik (2022) <doi:10.1090/mcom/3690> and Sablica and Hornik (2024) <doi:10.1016/j.jmaa.2024.128262>.

This package provides functions, tutorials, and examples to preprocess, analyze, and visualize data from whole room indirect calorimeters (WRIC) by Maastricht Instruments, using the OmniCal software. Some functions may also work with WRICs from other manufacturers, though full functionality has only been validated for Maastricht Instruments devices.

Posterior inference for the shape parameter alpha and mean matrix mu in the model X_i ~ Wishart_p(2*alpha, Sigma), under both an improper prior and a proper Gamma/inverse-Wishart prior. The posterior mode is found via a Newton-within-EM algorithm and joint samples are drawn via rejection sampling.

The Wavelet Decomposition followed by Random Forest Regression (RF) models have been applied for time series forecasting. The maximum overlap discrete wavelet transform (MODWT) algorithm was chosen as it works for any length of the series. The series is first divided into training and testing sets. In each of the wavelet decomposed series, the supervised machine learning approach namely random forest was employed to train the model. This package also provides accuracy metrics in the form of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Ding et al. (2021) <DOI: 10.1007/s11356-020-12298-3>.

This package provides simulation tools for power analysis in two-arm clinical trials with hierarchical win ratio endpoints. The package simulates time-to-event, recurrent event, and continuous outcomes, applies prioritized pairwise win/loss scoring, and summarizes win ratio and Finkelstein-Schoenfeld test operating characteristics.

Evaluation of alternatives based on multiple criteria using weighted technique for Order preference by similarity to an ideal solution method. Reference: Hwang CL. (1981, ISBN:978-3-540-10558-9).

Lets you temporarily execute an expression or a local block with a different here() root in the here package. This is useful for sourcing code in other projects which expect the root directory of here() to be the project directory of those projects. This may be the case with git submodules for example.

Computes Bayesian wavelet shrinkage credible intervals for nonparametric regression. The method uses cumulants to derive Bayesian credible intervals for wavelet regression estimates. The first four cumulants of the posterior distribution of the estimates are expressed in terms of the observed data and integer powers of the mother wavelet functions. These powers are closely approximated by linear combinations of wavelet scaling functions at an appropriate finer scale. Hence, a suitable modification of the discrete wavelet transform allows the posterior cumulants to be found efficiently for any data set. Johnson transformations then yield the credible intervals themselves. Barber, S., Nason, G.P. and Silverman, B.W. (2002) <doi:10.1111/1467-9868.00332>.

This package implements detection for the number and locations of the change-points in a time series using the Wild Binary Segmentation and the Locally Stationary Wavelet model of Korkas and Fryzlewicz (2017) <doi:10.5705/ss.202015.0262>.

Calculates the water balance of starch potatoes from Normalized Distance Vegetation Index (NDVI) images, German Weather Service (DWD) reference evapotranspiration, German Weather Service RADOLAN precipitation data and irrigation information. For more details see Piernicke et al. (2025) <doi:10.3390/rs17183227>.

This package provides functions for determining the effect of data weights on the variance of survey data: users will load a data set which has a weights column, and the package will calculate the design effect (DEFF), weighting loss, root design effect (DEFT), effective sample size (ESS), and/or weighted margin of error.

Allows users to create weighted confusion matrices and accuracy metrics that help with the model selection process for classification problems, where distance from the correct category is important. The package includes several weighting schemes which can be parameterized, as well as custom configuration options. Furthermore, users can decide whether they wish to positively or negatively affect the accuracy score as a result of applying weights to the confusion matrix. Functions are included to calculate accuracy metrics for imbalanced data. Finally, wconf integrates well with the caret package, but it can also work standalone when provided data in matrix form. References: Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package" <doi:10.18637/jss.v028.i05> Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization (and the mewto R package)" <doi:10.2139/ssrn.3805911> Monahov, A. (2024) "Improved Accuracy Metrics for Classification with Imbalanced Data and Where Distance from the Truth Matters, with the wconf R Package" <doi:10.2139/ssrn.4802336> Starovoitov, V., Golub, Y. (2020). New Function for Estimating Imbalanced Data Classification Results. Pattern Recognition and Image Analysis, 295â 302 Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023) "A general framework for implementing distances for categorical variables" <doi:10.48550/arXiv.2301.02190>.

Meta testing is the ability to test a function without having to provide its parameter values. Those values will be generated, based on semantic naming of parameters, as introduced by package wyz.code.offensiveProgramming'. Value generation logic can be completed with your own data types and generation schemes. This to meet your most specific requirements and to answer to a wide variety of usages, from general use case to very specific ones. While using meta testing, it becomes easier to generate stress test campaigns, non-regression test campaigns and robustness test campaigns, as generated tests can be saved and reused from session to session. Main benefits of using wyz.code.metaTesting is ability to discover valid and invalid function parameter combinations, ability to infer valid parameter values, and to provide smart summaries that allows you to focus on dysfunctional cases.

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.

Allows to generate on-demand or by batch, any R documentation file, whatever is kind, data, function, class or package. It populates documentation sections, either automatically or by considering your input. Input code could be standard R code or offensive programming code. Documentation content completeness depends on the type of code you use. With offensive programming code, expect generated documentation to be fully completed, from a format and content point of view. With some standard R code, you will have to activate post processing to fill-in any section that requires complements. Produced manual page validity is automatically tested against R documentation compliance rules. Documentation language proficiency, wording style, and phrasal adjustments remains your job.

Generate wordsearch and crossword puzzles using custom lists of words (and clues). Make them easy or hard, and print them to solve offline with paper and pencil!

This package provides a set of wrappers intended to check, read and download information from the Wikimedia sources. It is specifically created to work with names of celebrities, in which case their information and statistics can be downloaded. Additionally, it also builds links and snippets to use in combination with the function gallery() in netCoin package.

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>.

This dataset was collected using a new four-arm within-study comparison design. The study aimed to examine the impact of a mathematics training intervention and a vocabulary study session on post-test scores in mathematics and vocabulary, respectively. The innovative four-arm within-study comparison design facilitates both experimental and quasi-experimental identification of average causal effects.

Download daily weather data recorded at airport weather stations using the National Centers for Environmental Information (NCEI) API <https://www.ncei.noaa.gov/support/access-search-service-api-user-documentation>.

Utility functions to convert between the Spatial classes specified by the package sp', and the well-known binary (WKB) representation for geometry specified by the Open Geospatial Consortium'. Supports Spatial objects of class SpatialPoints', SpatialPointsDataFrame', SpatialLines', SpatialLinesDataFrame', SpatialPolygons', and SpatialPolygonsDataFrame'. Supports WKB geometry types Point', LineString', Polygon', MultiPoint', MultiLineString', and MultiPolygon'. Includes extensions to enable creation of maps with TIBCO Spotfire'.

Explore data in the wpp2019 (or 2017, 2015, ...) package using a shiny interface.

R clients to the Web of Science and InCites <https://clarivate.com/products/data-integration/> APIs, which allow you to programmatically download publication and citation data indexed in the Web of Science and InCites databases.