Computation of the Wasserstein Bipolarization Index as described in Lee and Sobel (Forthcoming) <doi:10.48550/arXiv.2408.03331>. Provides both asymptotic (Sommerfeld, 2017 <https://ediss.uni-goettingen.de/bitstream/handle/11858/00-1735-0000-0023-3FA1-C/DissertationSommerfeldRev.pdf?sequence=1>) and bootstrap methods (Efron and Narasimhan, 2020 <doi:10.1080/10618600.2020.1714633>) for calculating confidence intervals.

This is a set of minimization tools (maximum likelihood estimation and least square fitting) to solve examples in the Johan Gabrielsson and Dan Weiner's book "Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications" 5th ed. (ISBN:9198299107). Examples include linear and nonlinear compartmental model, turn-over model, single or multiple dosing bolus/infusion/oral models, allometry, toxicokinetics, reversible metabolism, in-vitro/in-vivo extrapolation, enterohepatic circulation, metabolite modeling, Emax model, inhibitory model, tolerance model, oscillating response model, enantiomer interaction model, effect compartment model, drug-drug interaction model, receptor occupancy model, and rebound phenomena model.

Wavelet routines that calculate single sets of wavelet multiple regressions and correlations, and cross-regressions and cross-correlations from a multivariate time series. Dynamic versions of the routines allow the wavelet local multiple (cross-)regressions and (cross-)correlations to evolve over time.

This package provides estimation and inference for win statistics in cluster-randomized trials with prioritized (hierarchical) composite outcomes. Supported summaries include the win ratio, win odds, net benefit, and desirability of outcome ranking (DOOR), with variance estimation and testing procedures that account for within-cluster correlation. Methods are described in the accompanying manuscript (2026) <doi:10.48550/arXiv.2604.18341>.

This package provides functions for computing moments and coefficients related to the Beta-Wishart and Inverse Beta-Wishart distributions. It includes functions for calculating the expectation of matrix-valued functions of the Beta-Wishart distribution, coefficient matrices C_k and H_k, expectation of matrix-valued functions of the inverse Beta-Wishart distribution, and coefficient matrices \tildeC_k and \tildeH_k. For more details, refer Hillier and Kan (2024) <https://www-2.rotman.utoronto.ca/~kan/papers/wishmom.pdf>, "On the Expectations of Equivariant Matrix-valued Functions of Wishart and Inverse Wishart Matrices".

This package provides a collection of functions related to novel methods for estimating R(t), created by the lab of Professor Laura White. Currently implemented methods include two-step Bayesian back-calculation and now-casting for line-list data with missing reporting delays, adapted in STAN from Li (2021) <doi:10.1371/journal.pcbi.1009210>, and calculation of time-varying reproduction number assuming a flux between various adjacent states, adapted into STAN from Zhou (2021) <doi:10.1371/journal.pcbi.1010434>.

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

This package provides a large English words list and tools to find words by patterns. In particular, anagram finder and scrabble word finder.

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.

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.

Assessing predictive models of spatial data can be challenging, both because these models are typically built for extrapolating outside the original region represented by training data and due to potential spatially structured errors, with "hot spots" of higher than expected error clustered geographically due to spatial structure in the underlying data. Methods are provided for assessing models fit to spatial data, including approaches for measuring the spatial structure of model errors, assessing model predictions at multiple spatial scales, and evaluating where predictions can be made safely. Methods are particularly useful for models fit using the tidymodels framework. Methods include Moran's I ('Moran (1950) <doi:10.2307/2332142>), Geary's C ('Geary (1954) <doi:10.2307/2986645>), Getis-Ord's G ('Ord and Getis (1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from Ji and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement metrics from Willmott (1981) (<doi: 10.1080/02723646.1981.10642213>) and Willmott et al'. (2012) (<doi: 10.1002/joc.2419>), an implementation of the area of applicability methodology from Meyer and Pebesma (2021) (<doi:10.1111/2041-210X.13650>), and an implementation of multi-scale assessment as described in Riemann et al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).

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>.

An easy-to-use interface for interacting with WebDAV servers, including OwnCloud'. It simplifies the use of WebDAV methods such as COPY, MOVE, DELETE and others. With built-in authentication and request handling, it allows for easy management of files and directories over the WebDAV protocol.

Using a time-varying random parameters model developed in Koutchade et al., (2024) <https://hal.science/hal-04318163>, this package allows allocating variable input costs among crops produced by farmers based on panel data including information on input expenditure aggregated at the farm level and acreage shares. It also considers in fairly way the weighting data and can allow integrating time-varying and time-constant control variables.

This package provides tools for a wavelet-based approach to analyzing spatial synchrony, principally in ecological data. Some tools will be useful for studying community synchrony. See, for instance, Sheppard et al (2016) <doi: 10.1038/NCLIMATE2991>, Sheppard et al (2017) <doi: 10.1051/epjnbp/2017000>, Sheppard et al (2019) <doi: 10.1371/journal.pcbi.1006744>.

The shiny application Wallace is a modular platform for reproducible modeling of species niches and distributions. Wallace guides users through a complete analysis, from the acquisition of species occurrence and environmental data to visualizing model predictions on an interactive map, thus bundling complex workflows into a single, streamlined interface. An extensive vignette, which guides users through most package functionality can be found on the package's GitHub Pages website: <https://wallaceecomod.github.io/wallace/articles/tutorial-v2.html>.

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.

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>.

Logging of scripts suitable for clinical trials using Quarto to create nice human readable logs. whirl enables execution of scripts in batch, while simultaneously creating logs for the execution of each script, and providing an overview summary log of the entire batch execution.

Efficiently read and write Waveform (WAV) audio files <https://en.wikipedia.org/wiki/WAV>. Support for unsigned 8 bit Pulse-code modulation (PCM), signed 12, 16, 24 and 32 bit PCM and other encodings.

Analyzing pedigree data of wild populations. While primarily designed to process outputs from the COLONY (Jones & Wang (2010) <doi:10.1111/j.1755-0998.2009.02787.x>) pedigree reconstruction software, it can also accommodate data from other sources. By linking reconstructed pedigrees with genetic sample metadata, wpeR produces spatial and temporal visualizations as well as tabular summaries that support interpretation of family structures and dynamics. The main goal of the package is to provide a solution for the analysis of complex wild pedigree data and to help the user to gain insights into genetic relationships within wild animal populations.

Entropy weighted k-means (ewkm) by Liping Jing, Michael K. Ng and Joshua Zhexue Huang (2007) <doi:10.1109/TKDE.2007.1048> is a weighted subspace clustering algorithm that is well suited to very high dimensional data. Weights are calculated as the importance of a variable with regard to cluster membership. The two-level variable weighting clustering algorithm tw-k-means (twkm) by Xiaojun Chen, Xiaofei Xu, Joshua Zhexue Huang and Yunming Ye (2013) <doi:10.1109/TKDE.2011.262> introduces two types of weights, the weights on individual variables and the weights on variable groups, and they are calculated during the clustering process. The feature group weighted k-means (fgkm) by Xiaojun Chen, Yunminng Ye, Xiaofei Xu and Joshua Zhexue Huang (2012) <doi:10.1016/j.patcog.2011.06.004> extends this concept by grouping features and weighting the group in addition to weighting individual features.

Analyzes and models data subject to sampling biases. Provides functions to estimate the density and cumulative distribution functions from biased samples of continuous distributions. Includes the estimators proposed by Bhattacharyya et al. (1988) <doi:10.1080/03610928808829825> and Jones (1991) <doi:10.2307/2337020> for density, and by Cox (2005, ISBN:052184939X) and Bose and Dutta (2022) <doi:10.1007/s00184-021-00824-3> for distribution, with different bandwidth selectors. Also includes a real length-biased dataset on shrub width from Muttlak (1988) <https://www.proquest.com/openview/3dd74592e623cdbcfa6176e85bd3d390/1?cbl=18750&diss=y&pq-origsite=gscholar>.

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>.