This package provides functions to assist in the processing and exploration of data from environmental monitoring programs. The package name stands for "water quality" and reflects the original focus on time series data for physical and chemical properties of water, as well as the biota. Intended for programs that sample approximately monthly, quarterly or annually at discrete stations, a feature of many legacy data sets. Most of the functions should be useful for analysis of similar-frequency time series regardless of the subject matter.

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.

This package provides functions to create factor variables with contrasts based on weighted effect coding, and their interactions. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different.

This package provides automated downloading, parsing and formatting of weather data for Australia through API endpoints provided by the Department of Primary Industries and Regional Development (DPIRD) of Western Australia and by the Science and Technology Division of the Queensland Government's Department of Environment and Science (DES). As well as the Bureau of Meteorology (BOM) of the Australian government precis and coastal forecasts, and downloading and importing radar and satellite imagery files. DPIRD weather data are accessed through public APIs provided by DPIRD, <https://www.dpird.wa.gov.au/online-tools/apis/>, providing access to weather station data from the DPIRD weather station network. Australia-wide weather data are based on data from the Australian Bureau of Meteorology (BOM) data and accessed through SILO (Scientific Information for Land Owners) Jeffrey et al. (2001) <doi:10.1016/S1364-8152(01)00008-1>. DPIRD data are made available under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU) license <https://creativecommons.org/licenses/by/3.0/au/deed.en>. SILO data are released under a Creative Commons Attribution 4.0 International licence (CC BY 4.0) <https://creativecommons.org/licenses/by/4.0/>. BOM data are (c) Australian Government Bureau of Meteorology and released under a Creative Commons (CC) Attribution 3.0 licence or Public Access Licence (PAL) as appropriate, see <https://www.bom.gov.au/copyright> for further details.

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.

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.

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.

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.

This package creates interactive web maps using the JavaScript Leaflet library with base layers of The National Map ('TNM'). TNM services provide access to base geospatial information that describes the landscape of the United States and its territories. This package is dependent on, and intended to be used with, the leaflet package.

Supports systematic scrutiny, modification, and integration of data. The function status() counts rows that have missing values in grouping columns (returned by na() ), have non-unique combinations of grouping columns (returned by dup() ), and that are not locally sorted (returned by unsorted() ). Functions enumerate() and itemize() give sorted unique combinations of columns, with or without occurrence counts, respectively. Function ignore() drops columns in x that are present in y, and informative() drops columns in x that are entirely NA; constant() returns values that are constant, given a key. Data that have defined unique combinations of grouping values behave more predictably during merge operations.

Interface to the World Inequality Database (WID) API <https://wid.world>. Downloads distributional national accounts data with filters for country, year, percentile, age group, and population type. Includes code validation and reference tables. Independent implementation unaffiliated with the World Inequality Lab (WIL) or the Paris School of Economics.

Shinohara (2014) <doi:10.1016/j.nicl.2014.08.008> introduced WhiteStripe', an intensity-based normalization of T1 and T2 images, where normal appearing white matter performs well, but requires segmentation. This method performs white matter mean and standard deviation estimates on data that has been rigidly-registered to the MNI template and uses histogram-based methods.

This package provides tools for extracting and analyzing cyclic signals from time series.

The main functionalities of wrappedtools are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well.

This estimates precise weaning ages for a given skeletal population by analyzing the stable nitrogen isotope ratios of them. Bone collagen turnover rates estimated anew and the approximate Bayesian computation (ABC) were adopted in this package.

This package implements the diagnostic "theta" developed in Poetscher and Preinerstorfer (2020) "How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?" <doi:10.48550/arXiv.2005.04089>, which appeared as <doi:10.1017/S0266466622000184> in Econometric Theory , Volume 39 , Issue 4 , August 2023 , pp. 789 - 847. The diagnostic "theta" can be used to detect and weed out bootstrap-based procedures that provably have size equal to one for a given testing problem. The implementation covers a large variety of bootstrap-based procedures, cf. the above mentioned article for details. A function for computing bootstrap p-values is provided.

This package performs an analysis of time-to-event clinical trial data using various "win time" methods, including ewt', ewtr', rmt', ewtp', rewtp', ewtpr', rewtpr', max', wtr', rwtr', pwt', and rpwt'. These methods are used to calculate and compare treatment effects on ordered composite endpoints. The package handles event times, event indicators, and treatment arm indicators and supports calculations on observed and resampled data. Detailed explanations of each method and usage examples are provided in "Use of win time for ordered composite endpoints in clinical trials," by Troendle et al. (2024)<doi:10.1002/sim.10045>. For more information, see the package documentation or the vignette titled "Introduction to wintime.".

This package provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) "When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?", available at <https://imai.fas.harvard.edu/research/FEmatch.html>.

Allows to turn standard R code into offensive programming code. Provides code instrumentation to ease this change and tools to assist and accelerate code production and tuning while using offensive programming code technics. Should improve code robustness and quality. Function calls can be easily verified on-demand or in batch mode to assess parameter types and length conformities. Should improve coders productivity as offensive programming reduces the code size due to reduced number of controls all along the call chain. Should speed up processing as many checks will be reduced to one single check.

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>.

This package provides a parallel implementation of Weighted Subspace Random Forest. The Weighted Subspace Random Forest algorithm was proposed in the International Journal of Data Warehousing and Mining by Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye (2012) <DOI:10.4018/jdwm.2012040103>. The algorithm can classify very high-dimensional data with random forests built using small subspaces. A novel variable weighting method is used for variable subspace selection in place of the traditional random variable sampling.This new approach is particularly useful in building models from high-dimensional data.

This package provides data from the United Nation's World Population Prospects 2015.

Separates supplied variables into within- and between-cluster components and calculates bivariate correlations for each level separately. For Pearson correlations, the centered-score decomposition corresponds to commonly used between- and within-cluster correlations reviewed by Tu et al. (2025) <doi:10.1002/sim.10326>. The package's descriptive Spearman option is distinct from the clustered rank parameters introduced in that paper. The package is also motivated by the distinction between within- and between-person variation described by Curran and Bauer (2011) <doi:10.1146/annurev.psych.093008.100356> and by Hamaker (2024) <doi:10.1080/00273171.2022.2155930>. The package is intended for longitudinal or otherwise clustered data where researchers need transparent correlation matrices before fitting more complex multilevel models.

This package provides tools for simulating the biophysical effects of vessel-strikes on whales. The aim is to support the evaluation of marine policies limiting ship speeds through regions in which whales reside. This is important because ship strikes are a major source of lethality for several whale species, including the critically endangered North Atlantic right whale. In this analysis, whales are modelled with a four-layer system comprising skin, blubber, sub-layer (muscle or organ) and bone. Reasonable values for the material properties of these layers, along with other factors such as whale surface area and mass, are provided for a variety of whale species. Similarly, key values are provided for several ship types. The collision is modelled according to Newtonian dynamics, with stresses and strains within the whale layers being simulated over time. The simulation results are analyzed in the context of whale-strike data, to develop a Lethality Index for the whale in the modelled collision. For the underlying science, see Kelley and other "Assessing the Lethality of Ship Strikes on Whales Using Simple Biophysical Models." (2021) <doi:10.1111/mms.12745>. For more on the R code, see Kelley "`whalestrike`: An R package for simulating ship strikes on whales" (2024) <doi:10.21105/joss.06473>.