In Multidimensional Systems the When dimension allows us to express when the analysed facts have occurred. The purpose of this package is to provide support for implementing this dimension in the form of date and time tables for Relational On-Line Analytical Processing star database systems.

This package provides a collection of utility functions, themes, and templates to support personal data analysis workflows. Includes functions for formatting numeric values as text, custom themes and color scales for ggplot2', and automatic formatting for tables created with gt'.

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>.

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.

Simulates response-adaptive experimental trials using Multi-Arm Bandits. Adaptive robust estimators defined in Hadad et al. (2021) <doi:10.1073/pnas.2014602118> and Offer-Westort et al. (2021) <doi:10.1111/ajps.12597> are used to robustly estimate conditional expectations and treatment effects. Provides significant simulation customization options for imperfect information, non-stationary bandits, and increased exploration strategies for assignments.

Descriptive statistics for large data tend to be low resolution on the tails. Whisker Odds generate a table of descriptive statistics for large data. This is the same as letter-values, but with an alternative naming of depths which allow for depths beyond 26. For a reference to letter-values see Heike Hofmann and Hadley Wickham and Karen Kafadar (2017) <doi:10.1080/10618600.2017.1305277>.

Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from lme4pureR (Walker and Bates (2013) <https://github.com/lme4/lme4pureR>).

Opinionated functions that enable easier and faster analysis of Viva Insights data. There are three main types of functions in wpa': (i) Standard functions create a ggplot visual or a summary table based on a specific Viva Insights metric; (2) Report Generation functions generate HTML reports on a specific analysis area, e.g. Collaboration; (3) Other miscellaneous functions cover more specific applications (e.g. Subject Line text mining) of Viva Insights data. This package adheres to tidyverse principles and works well with the pipe syntax. wpa is built with the beginner-to-intermediate R users in mind, and is optimised for simplicity.

This package performs Wilcoxon-Mann-Whitney test in the presence of missing data with controlled Type I error regardless of the values of missing data.

Lets you temporarily execute an expression or a local block with a different here() root in the here package. This is useful for sourcing code in other projects which expect the root directory of here() to be the project directory of those projects. This may be the case with git submodules for example.

This package provides a set of tools for processing and analyzing data developed in the context of the "Who Has Eaten the Planet" (WHEP) project, funded by the European Research Council (ERC). For more details on multi-regional inputâ output model "Food and Agriculture Biomass Inputâ Output" (FABIO) see Bruckner et al. (2019) <doi:10.1021/acs.est.9b03554>.

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.

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.

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 german Wikibook "GNU R" introduces R to new users. This package is a collection of functions and datas used in the german WikiBook "GNU R".

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 two functions frameableWidget()', and frameWidget()'. The frameableWidget() is used to add extra code to a htmlwidget which allows is to be rendered correctly inside a responsive iframe'. The frameWidget() is a htmlwidget which displays content of another htmlwidget inside a responsive iframe'. These functions allow for easier embedding of htmlwidgets in content management systems such as wordpress', blogger etc. They also allow for separation of widget content from main HTML content where CSS of the main HTML could interfere with the widget.

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

Encapsulates the pattern of untidying data into a wide matrix, performing some processing, then turning it back into a tidy form. This is useful for several operations such as co-occurrence counts, correlations, or clustering that are mathematically convenient on wide matrices.

This package provides a set of utility function to prevent the spread of utility scripts in W4M (Workflow4Metabolomics) tools, and centralize them in a single package. To note, some are meant to be replaced by the use of dedicated packages in the future, like the parse_args() function: it is here only to prepare the ground for more global changes in W4M scripts and tools. This package is used by part of the W4M Galaxy modules, some of them being available on the community-maintained GitHub repository for Metabolomics Galaxy tools <https://github.com/workflow4metabolomics/tools-metabolomics>. See Delporte et al (2025) <doi:10.1002/cpz1.70095> for more details.

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>.

Originally designed application in the context of resource-limited plant research and breeding programs, waves provides an open-source solution to spectral data processing and model development by bringing useful packages together into a streamlined pipeline. This package is wrapper for functions related to the analysis of point visible and near-infrared reflectance measurements. It includes visualization, filtering, aggregation, preprocessing, cross-validation set formation, model training, and prediction functions to enable open-source association of spectral and reference data. This package is documented in a peer-reviewed manuscript in the Plant Phenome Journal <doi:10.1002/ppj2.20012>. Specialized cross-validation schemes are described in detail in Jarquà n et al. (2017) <doi:10.3835/plantgenome2016.12.0130>. Example data is from Ikeogu et al. (2017) <doi:10.1371/journal.pone.0188918>.

Wrap-around Time Series (WATS) plots for interrupted time series designs with seasonal patterns. Longitudinal trajectories are shown in both Cartesian and polar coordinates. In many scenarios, a WATS plot more clearly shows the existence and effect size of of an intervention. This package accompanies "Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs" by Rodgers, Beasley, & Schuelke (2014) <doi:10.1080/00273171.2014.946589>; see citation("Wats") for details.

Weighted Piecewise Kernel Density Estimation for large data.