This package provides data from the United Nation's World Population Prospects 2017.

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 functions for analysing Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus interactions in agricultural production systems. Includes functions for computing water use efficiency (WUE), water productivity (WP), and water footprint (WF) including green, blue, and grey components following the methodology of Hoekstra et al. (2011, ISBN:9781849712798). Includes energy budgeting tools for energy use efficiency (EUE), energy return on investment (EROI), net energy (NE), and energy productivity (EP). Computes nutrient use efficiency (NUE) metrics including agronomic efficiency (AE), physiological efficiency (PE), recovery efficiency (RE), and partial factor productivity (PFP) as defined by Dobermann (2007) <https://digitalcommons.unl.edu/agronomyfacpub/316/> and Congreves et al. (2021) <doi:10.3389/fpls.2021.637108>. Estimates carbon footprint (CF), greenhouse gas (GHG) emissions, soil organic carbon (SOC) stocks, and global warming potential (GWP) using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) default values (CH4 = 27, N2O = 273) as reported in Forster et al. (2021) <doi:10.1017/9781009157896.009>. Computes composite Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus indices, trade-off correlation matrices, and generates radar and heatmap visualizations for comparing agricultural treatments. Supports conservation agriculture (CA), irrigated and rain-fed systems, and arid and semi-arid production environments. Methods follow Lal (2004) <doi:10.1016/j.envint.2004.03.005> for carbon emissions from farm operations, and Hoover et al. (2023) <doi:10.1016/j.scitotenv.2022.160992> for water use efficiency indicators.

This package provides a toolbox of common robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. It is also available as a module for jamovi (see <https://www.jamovi.org> for more information). Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book Introduction to Robust Estimation & Hypothesis Testing'.

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.

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.

Inferences about counterfactuals are essential for prediction, answering what if questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Unfortunately, standard statistical approaches assume the veracity of the model rather than revealing the degree of model-dependence, which makes this problem hard to detect. WhatIf offers easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. If an analysis fails the tests offered here, then we know that substantive inferences will be sensitive to at least some modeling choices that are not based on empirical evidence, no matter what method of inference one chooses to use. WhatIf implements the methods for evaluating counterfactuals discussed in Gary King and Langche Zeng, 2006, "The Dangers of Extreme Counterfactuals," Political Analysis 14 (2) <DOI:10.1093/pan/mpj004>; and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The Pitfalls of Counterfactual Inference," International Studies Quarterly 51 (March) <DOI:10.1111/j.1468-2478.2007.00445.x>.

This package provides a WebSocket client interface for R. WebSocket is a protocol for low-overhead real-time communication: <https://en.wikipedia.org/wiki/WebSocket>.

R interface to the weightederm package for Python', which provides scikit-learn'-style estimators for offline change point regression (data segmentation) via weighted empirical risk minimization. Supports least-squares, Huber, and logistic losses with fixed or cross-validated numbers of change points. Wraps Python via reticulate'. Arpino and Venkataramanan (2026) <doi:10.48550/arXiv.2604.11746>.

This package provides functions to convert between weather metrics, including conversions for metrics of temperature, air moisture, wind speed, and precipitation. This package also includes functions to calculate the heat index from air temperature and air moisture.

Lossless webp images are 26% smaller in size compared to PNG. Lossy webp images are 25-34% smaller in size compared to JPEG. This package reads and writes webp images into a 3 (rgb) or 4 (rgba) channel bitmap array using conventions from the jpeg and png packages.

This package provides efficient implementations of weighted dependence measures and related asymptotic tests for independence. Implemented measures are the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, Hoeffding's D, and Chatterjee's xi; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0>, Hollander et al. (2015, ISBN:9780470387375), and Chatterjee (2021) <doi:10.1080/01621459.2020.1758115>.

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'.

This package provides a collection of white noise hypothesis tests for functional time series and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models. These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019) <doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, and Kim et al. (2023) <doi: 10.1214/23-SS143> respectively.

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.

ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.

This package provides tools for generating simulated sawn timber strength grading data with a main focus on statistical simulation based on covariance matrices. Simulation data for Norway spruce sawn timber from Austria and reference values of means and standard deviations of grade determining properties from literature for a number of European countries are provided, as well.

The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test. This package can be used with any test dataset on which you have computed prior probabilities of case status, posterior probabilities of case status, and you have the observed case-control status. In comparison with the C-statistic (area under ROC curve), the expected weight of evidence (expected information for discrimination) has several advantages as a summary measure of predictive performance. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.

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>.

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>.

Efficient Bayesian generalized linear models with time-varying coefficients as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for efficient sampling. For non-Gaussian models, the package uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).

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>.

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>.

An implementation of the Weighted Portmanteau Tests described in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing" published by the Journal of the American Statistical Association, Volume 107, Issue 498, pages 777-787, 2012.