Set of functions that improves the graphical presentations of the functions: wave.correlation and spin.correlation (waveslim package, Whitcher 2012) and the wave.multiple.correlation and wave.multiple.cross.correlation (wavemulcor package, Fernandez-Macho 2012b). The plot outputs (heatmaps) can be displayed in the screen or can be saved as PNG or JPG images or as PDF or EPS formats. The W2CWM2C package also helps to handle the (input data) multivariate time series easily as a list of N elements (times series) and provides a multivariate data set (dataexample) to exemplify its use. A description of the package was published in a scientific paper: Polanco-Martinez and Fernandez-Macho (2014), <doi:10.1109/MCSE.2014.96>.

Introduce weights into Ordered Weighted Averages and extend bivariate means based on n-ary tree construction. Please refer to the following: G. Beliakov, H. Bustince, and T. Calvo (2016, ISBN: 978-3-319-24753-3), G. Beliakov(2018) <doi:10.1002/int.21913>, G. Beliakov, J.J. Dujmovic (2016) <doi:10.1016/j.ins.2015.10.040>, J.J. Dujmovic and G. Beliakov (2017) <doi:10.1002/int.21828>.

The Model Disability Survey (MDS) <https://www.who.int/activities/collection-of-data-on-disability> is a World Health Organization (WHO) general population survey instrument to assess the distribution of disability within a country or region, grounded in the International Classification of Functioning, Disability and Health <https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health>. This package provides fit-for-purpose functions for calculating and presenting the results from this survey, as used by the WHO. The package primarily provides functions for implementing Rasch Analysis (see Andrich (2011) <doi:10.1586/erp.11.59>) to calculate a metric scale for disability.

Fast computation of Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) for weighted binary classification problems (weights are example-specific cost values).

This package provides a convenient data set, a set of helper functions, and a benchmark function for economically (profit) driven wind farm layout optimization. This enables researchers in the field of the NP-hard (non-deterministic polynomial-time hard) problem of wind farm layout optimization to focus on their optimization methodology contribution and also provides a realistic benchmark setting for comparability among contributions. See Croonenbroeck, Carsten & Hennecke, David (2020) <doi:10.1016/j.energy.2020.119244>.

This package provides a collection of tools to fit and work with trophic Species Distribution Models. Trophic Species Distribution Models combine knowledge of trophic interactions with Bayesian structural equation models that model each species as a function of its prey (or predators) and environmental conditions. It exploits the topological ordering of the known trophic interaction network to predict species distribution in space and/or time, where the prey (or predator) distribution is unavailable. The method implemented by the package is described in Poggiato, Andréoletti, Pollock and Thuiller (2022) <doi:10.22541/au.166853394.45823739/v1>.

Access and analyze the World Bank's International Debt Statistics (IDS) <https://www.worldbank.org/en/programs/debt-statistics/ids>. IDS provides creditor-debtor relationships between countries, regions, and institutions. wbids enables users to download, process and work with IDS series across multiple geographies, counterparts, and time periods.

The main purpose of waterquality is to quickly and easily convert satellite-based reflectance imagery into one or many well-known water quality algorithms designed for the detection of harmful algal blooms or the following pigment proxies: chlorophyll-a, blue-green algae (phycocyanin), and turbidity. Johansen et al. (2019) <doi:10.21079/11681/35053>.

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>.

Utilities for using a probability sample to reweight prevalence estimates calculated from the All of Us research program. Weighted estimates will still not be representative of the general U.S. population. However, they will provide an early indication for how unweighted estimates may be biased by the sampling bias in the All of Us sample.

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

An adaptation for estuaries (tidal waters) of weighted regression on time, discharge, and season to evaluate trends in water quality time series. Please see Beck and Hagy (2015) <doi:10.1007/s10666-015-9452-8> for details.

This package provides a single function to fit data of an input data frame into one of the selected Weibull functions (w2, w3 and it's truncated versions), calculating the scale, location and shape parameters accordingly. The resulting plots and files are saved into the folder parameter provided by the user. References: a) John C. Nash, Ravi Varadhan (2011). "Unifying Optimization Algorithms to Aid Software System Users: optimx for R" <doi:10.18637/jss.v043.i09>.

This package provides statistical methods and visualizations that are often used in reliability engineering. Comprises a compact and easily accessible set of methods and visualization tools that make the examination and adjustment as well as the analysis and interpretation of field data (and bench tests) as simple as possible. Non-parametric estimators like Median Ranks, Kaplan-Meier (Abernethy, 2006, <ISBN:978-0-9653062-3-2>), Johnson (Johnson, 1964, <ISBN:978-0444403223>), and Nelson-Aalen for failure probability estimation within samples that contain failures as well as censored data are included. The package supports methods like Maximum Likelihood and Rank Regression, (Genschel and Meeker, 2010, <DOI:10.1080/08982112.2010.503447>) for the estimation of multiple parametric lifetime distributions, as well as the computation of confidence intervals of quantiles and probabilities using the delta method related to Fisher's confidence intervals (Meeker and Escobar, 1998, <ISBN:9780471673279>) and the beta-binomial confidence bounds. If desired, mixture model analysis can be done with segmented regression and the EM algorithm. Besides the well-known Weibull analysis, the package also contains Monte Carlo methods for the correction and completion of imprecisely recorded or unknown lifetime characteristics. (Verband der Automobilindustrie e.V. (VDA), 2016, <ISSN:0943-9412>). Plots are created statically ('ggplot2') or interactively ('plotly') and can be customized with functions of the respective visualization package. The graphical technique of probability plotting as well as the addition of regression lines and confidence bounds to existing plots are supported.

Obtain information on peak flow data from the National River Flow Archive (NRFA) in the United Kingdom, either from the Peak Flow Dataset files <https://nrfa.ceh.ac.uk/data/peak-flow-dataset> once these have been downloaded to the user's computer or using the NRFA's API. These files are in a format suitable for direct use in the WINFAP software, hence the name of the package.

Non- and semiparametric regression for generalized additive, partial linear, and varying coefficient models as well as their combinations via smoothed backfitting. Based on Roca-Pardinas J and Sperlich S (2010) <doi:10.1007/s11222-009-9130-2>; Mammen E, Linton O and Nielsen J (1999) <doi:10.1214/aos/1017939138>; Lee YK, Mammen E, Park BU (2012) <doi:10.1214/12-AOS1026>.

Data from the United Nation's World Population Prospects 2010.

Collect multichannel marketing data from sources such as Google analytics, Facebook Ads, and many others using the Windsor.ai API <https://www.windsor.ai/api-fields/>.

Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, developed under the methodology of Jeong, J., Lee, K. (1999) <https://yonsei.pure.elsevier.com/en/publications/bootstrapped-whites-test-for-heteroskedasticity-in-regression-mod>.

Estimates the standard and weighted Elo (WElo, Angelini et al., 2022 <doi:10.1016/j.ejor.2021.04.011>) rates. The current version provides Elo and WElo rates for tennis, according to different systems of weights (games or sets) and scale factors (constant, proportional to the number of matches, with more weight on Grand Slam matches or matches played on a specific surface). Moreover, the package gives the possibility of estimating the (bootstrap) standard errors for the rates. Finally, the package includes betting functions that automatically select the matches on which place a bet.

Create dense vector representation of words and documents using quanteda'. Currently implements Word2vec (Mikolov et al., 2013) <doi:10.48550/arXiv.1310.4546> and Latent Semantic Analysis (Deerwester et al., 1990) <doi:10.1002/(SICI)1097-4571(199009)41:6%3C391::AID-ASI1%3E3.0.CO;2-9>.

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>).

Takes screenshots of web pages, including Shiny applications and R Markdown documents. webshot2 uses headless Chrome or Chromium as the browser back-end.

K-means clustering, hierarchical clustering, and PCA with observational weights and/or variable weights. It also includes the corresponding functions for data nuggets which serve as representative samples of large datasets. Cherasia et al., (2022) <doi:10.1007/978-3-031-22687-8_20>. Amaratunga et al., (2009) <doi:10.1002/9780470317129>.