Use the what3words API <https://developer.what3words.com/public-api> to return three words which uniquely identify every 3m x 3m square on Earth. It is also possible to return coordinates from any valid three words location. Supports multiple languages.

Calculate the win ratio for prioritized outcomes and the 95% confidence interval based on Bebu and Lachin (2016) <doi:10.1093/biostatistics/kxv032>. Three type of outcomes can be analyzed: survival "failure-time" events, repeated survival "failure-time" events and continuous or ordinal "non-failure time" events that are captured at specific time-points in the study.

This package provides Water Year Hydrologic Classification Indices based on measured unimpaired runoff (in million acre-feet). Data is provided by California Department of Water Resources and subject to revision.

The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) <DOI: 10.1109/INFOCT.2018.8356851>.

The outcome of various rehabilitation strategies for water distribution systems can be modeled with the Water Management Simulator (WaMaSim). Pipe breaks and the corresponding damage and rehabilitation costs are simulated. It is mainly intended to be used as educational tool for the Water Infrastructure Experimental and Computer Laboratory at ETH Zurich, Switzerland.

Applies the item weighting method from Kilic & Dogan (2019) <doi:10.21031/epod.516057>. To improve construct validity, this method re-computes scores by utilizing the item discrimination index in conjunction with a condition established upon person ability and item difficulty.

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>.

This package provides tools for weighted spatial tessellation using Euclidean and geodesic distances within constrained polygonal domains. The package can generate complete and connected spatial partitions that respect complex boundaries, heterogeneous point weights, and optional resistance or terrain effects. The methods extend weighted Voronoi tessellations to constrained domains and graph-based cost-distance surfaces. For background see Aurenhammer (1991) <doi:10.1145/116873.116880> and van Etten (2017) <doi:10.18637/jss.v076.i13>.

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.

Post-construction fatality monitoring studies at wind facilities are based on data from searches for bird and bat carcasses in plots beneath turbines. Bird and bat carcasses can fall outside of the search plot. Bird and bat carcasses from wind turbines often fall outside of the searched area. To compensate, area correction (AC) estimations are calculated to estimate the percentage of fatalities that fall within the searched area versus those that fall outside of it. This package provides two likelihood based methods and one physics based method (Hull and Muir (2010) <doi:10.1080/14486563.2010.9725253>, Huso and Dalthorp (2014) <doi:10.1002/jwmg.663>) to estimate the carcass fall distribution. There are also functions for calculating the proportion of area searched within one unit annuli, log logistic distribution functions, and truncated distribution functions.

Makes research involving EMDAT and related datasets easier. These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions.

Within-subject mediation analysis using structural equation modeling. Examine how changes in an outcome variable between two conditions are mediated through one or more variables. Supports within-subject mediation analysis using the lavaan package by Rosseel (2012) <doi:10.18637/jss.v048.i02>, and extends Monte Carlo confidence interval estimation to missing data scenarios using the semmcci package by Pesigan and Cheung (2023) <doi:10.3758/s13428-023-02114-4>.

Analyze given data frame with multiple endpoints and return Kaplan-Meier survival probabilities together with the specified confidence interval. See Nabipoor M, Westerhout CM, Rathwell S, and Bakal JA (2023) <doi:10.1186/s12874-023-01857-0>.

Displays geospatial data on an interactive 3D globe in the web browser.

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.

Calculates non-parametric estimates of the sample size, power and confidence intervals for the win-ratio. For more detail on the theory behind the methodologies implemented see Yu, R. X. and Ganju, J. (2022) <doi:10.1002/sim.9297>.

Allows to generate automatically testthat code files from offensive programming test cases. Generated test files are complete and ready to run. Using wyz.code.testthat you will earn a lot of time, reduce the number of errors in test case production, be able to test immediately generated files without any need to view or modify them, and enter a zero time latency between code implementation and industrial testing. As with testthat', you may complete provided test cases according to your needs to push testing further, but this need is nearly void when using wyz.code.offensiveProgramming'.

Evaluation of prediction performance of smaller regions of spectra for Chemometrics. Segmentation of spectra, evolving dimensions regions and sliding windows as selection methods. Election of the best model among those computed based on error metrics. Chen et al.(2017) <doi:10.1007/s00216-017-0218-9>.

Data structures and methods to work with web tracking data. The functions cover data preprocessing steps, enriching web tracking data with external information and methods for the analysis of digital behavior as used in several academic papers (e.g., Clemm von Hohenberg et al., 2023 <doi:10.17605/OSF.IO/M3U9P>; Stier et al., 2022 <doi:10.1017/S0003055421001222>).

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>.

Datasets from the WallOmics project. Contains phenomics, metabolomics, proteomics and transcriptomics data collected from two organs of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. Exploratory and integrative analyses of these data are presented in Durufle et al (2020) <doi:10.1093/bib/bbaa166> and Durufle et al (2020) <doi:10.3390/cells9102249>.

The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of limma is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too.

Download and plot education specific demographic data from the Wittgenstein Centre for Demography and Human Capital Data Explorer <https://dataexplorer.wittgensteincentre.org/>.

The BACON algorithms are methods for multivariate outlier nomination (detection) and robust linear regression by Billor, Hadi, and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>. The extension to weighted problems is due to Beguin and Hulliger (2008) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X200800110616>; see also <doi:10.21105/joss.03238>.