This package provides functions to prepare and analyse eye tracking data of reading exercises. The functions allow some basic data preparations and code fixations as first and second pass. First passes can be further devided into forward and reading. The package further allows for aggregating fixation times per AOI or per AOI and per type of pass (first forward, first rereading, second). These methods are based on Hyönä, Lorch, and Rinck (2003) <doi:10.1016/B978-044451020-4/50018-9> and Hyönä, and Lorch (2004) <doi:10.1016/j.learninstruc.2004.01.001>. It is also possible to convert between metric length and visual degrees.

We quantitatively evaluated the assertion that says if one suit is found to be evenly distributed among the 4 players, the rest of the suits are more likely to be evenly distributed. Our mathematical analyses show that, if one suit is found to be evenly distributed, then a second suit has a slightly elevated probability (ranging between 10% to 15%) of being evenly distributed. If two suits are found to be evenly distributed, then a third suit has a substantially elevated probability (ranging between 30% to 50%) of being evenly distributed.This package refers to methods and authentic data from Ely Culbertson <https://www.bridgebum.com/law_of_symmetry.php>, Gregory Stoll <https://gregstoll.com/~gregstoll/bridge/math.html>, and details of performing the probability calculations from Jeremy L. Martin <https://jlmartin.ku.edu/~jlmartin/bridge/basics.pdf>, Emile Borel and Andre Cheron (1954) "The Mathematical Theory of Bridge",Antonio Vivaldi and Gianni Barracho (2001, ISBN:0 7134 8663 5) "Probabilities and Alternatives in Bridge", Ken Monzingo (2005) "Hand and Suit Patterns" <http://web2.acbl.org/documentlibrary/teachers/celebritylessons/handpatternsrevised.pdf>Ken Monzingo (2005) "Hand and Suit Patterns" <http://web2.acbl.org/documentlibrary/teachers/celebritylessons/handpatternsrevised.pdf>.

Computes a series of indices commonly used in the fields of economic geography, economic complexity, and evolutionary economics to describe the location, distribution, spatial organization, structure, and complexity of economic activities. Functions include basic spatial indicators such as the location quotient, the Krugman specialization index, the Herfindahl or the Shannon entropy indices but also more advanced functions to compute different forms of normalized relatedness between economic activities or network-based measures of economic complexity. Most of the functions use matrix calculus and are based on bipartite (incidence) matrices consisting of region - industry pairs. These are described in Balland (2017) <http://econ.geo.uu.nl/peeg/peeg1709.pdf>.

This package provides functions that help with analysis of prognostic study data. This allows users with little experience of developing models to develop models and assess the performance of the prognostic models. This also summarises the information, so the performance of multiple models can be displayed simultaneously. This minor update fixes issues related to memory requirements with large number of simulations and deals with situations when there is overfitting of data. Gurusamy, K (2026)<https://github.com/kurinchi2k/EQUALPrognosis>.

Rolling and expanding window approaches to assessing abundance based early warning signals, non-equilibrium resilience measures, and machine learning. See Dakos et al. (2012) <doi:10.1371/journal.pone.0041010>, Deb et al. (2022) <doi:10.1098/rsos.211475>, Drake and Griffen (2010) <doi:10.1038/nature09389>, Ushio et al. (2018) <doi:10.1038/nature25504> and Weinans et al. (2021) <doi:10.1038/s41598-021-87839-y> for methodological details. Graphical presentation of the outputs are also provided for clear and publishable figures. Visit the EWSmethods website for more information, and tutorials.

This package provides a meta-package that installs and loads a set of packages from easystats ecosystem in a single step. This collection of packages provide a unifying and consistent framework for statistical modeling, visualization, and reporting. Additionally, it provides articles targeted at instructors for teaching easystats', and a dashboard targeted at new R users for easily conducting statistical analysis by accessing summary results, model fit indices, and visualizations with minimal programming.

Evaluate diagnostic test performance using data from laboratory or diagnostic research. It supports both binary and continuous test variables. It allows users to compute key performance indicators and visualize Receiver Operating Characteristic (ROC) curves, determine optimal cut-off thresholds, display confusion matrix, and export publication-ready plot. It aims to facilitate the application of statistical methods in diagnostic test evaluation by healthcare professionals. The methodology used to compute the performance indicators follows the overview described by Habibzadeh (2025) <doi:10.11613/BM.2025.010101>. Thanks to shiny package.

This package provides easy access to tidy education finance data using Bellwether's methodology to combine NCES F-33 Survey, Census Bureau Small Area Income Poverty Estimates (SAIPE), and community data from the ACS 5-Year Estimates. The package simplifies downloading, caching, and filtering education finance data by year and state, enabling researchers and analysts to explore K-12 education funding patterns, revenue sources, expenditure categories, and demographic factors across U.S. school districts.

This package provides functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises.

Facilitates access to sample datasets from the EunomiaDatasets repository (<https://github.com/ohdsi/EunomiaDatasets>).

This package provides functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) <doi:10.1016/j.anbehav.2011.07.016> for an introduction.

Given two samples of size n_1 and n_2 from a data set where each sample consists of K functional observations (channels), each recorded on T grid points, the function energy method implements a hypothesis test of equality of channel-wise mean at each channel using the bootstrapped distribution of maximum energy to control family wise error. The function energy_method_complex accomodates complex valued functional observations.

The extended neighbourhood rule for the k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan (2022) <doi:10.48550/arXiv.2205.15111>.

This package implements the exponential Factor Copula Model (eFCM) of Castro-Camilo, D. and Huser, R. (2020) for spatial extremes, with tools for dependence estimation, tail inference, and visualization. The package supports likelihood-based inference, Gaussian process modeling via Matérn covariance functions, and bootstrap uncertainty quantification. See Castro-Camilo and Huser (2020) <doi:10.1080/01621459.2019.1647842>.

An implementation of European Forestry Dynamics Model (EFDM) and an estimation algorithm for the transition probabilities. The EFDM is a large-scale forest model that simulates the development of the forest and estimates volume of wood harvested for any given forested area. This estimate can be broken down by, for example, species, site quality, management regime and ownership category. See Packalen et al. (2015) <doi:10.2788/153990>.

The elliptical factor model, as an extension of the traditional factor model, effectively overcomes the limitations of the traditional model when dealing with heavy-tailed characteristic data. This package implements sparse principal component methods (SPC) and bi-sparse online principal component estimation (SPOC) for parameter estimation. Includes functionality for calculating mean squared error, relative error, and loading matrix sparsity.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

The purpose of this package is to support the setup the R environment. The two main features are autos', to automatically source files and/or directories into your environment, and paths to consistently set path objects across projects for input and output. Both are implemented using a configuration file to allow easy, custom configurations that can be used for multiple or all projects.

Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) <doi:10.1007/978-3-540-39804-2_12>, Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) <doi:10.1109/TSMCA.2009.2029559>, R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) <doi:10.1007/s10044-003-0192-z>, Shuo Wang and Xin Yao (2009) <doi:10.1109/CIDM.2009.4938667>, Yoav Freund and Robert E. Schapire (1997) <doi:10.1006/jcss.1997.1504>.

Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. <doi:10.22271/09746315.2020.v16.i3.1358>.

Checks to see whether a supplied set of dice (their face values) are transitive, returning pair-win and group-roll win probabilities. Expected returns (mean magnitude of win/loss) are presented as well.

This package provides a function for distribution free control chart based on the change point model, for multivariate statistical process control. The main constituent of the chart is the energy test that focuses on the discrepancy between empirical characteristic functions of two random vectors. This new control chart highlights in three aspects. Firstly, it is distribution free, requiring no knowledge of the random processes. Secondly, this control chart can monitor mean and variance simultaneously. Thirdly it is devised for multivariate time series which is more practical in real data application. Fourthly, it is designed for online detection (Phase II), which is central for real time surveillance of stream data. For more information please refer to O. Okhrin and Y.F. Xu (2017) <https://github.com/YafeiXu/working_paper/raw/master/CPM102.pdf>.

Some utility functions for validation and data manipulation. These functions can be helpful to reduce internal codes everywhere in package development.

This package provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.

Programmatic interface to the European Centre for Medium-Range Weather Forecasts dataset web services (ECMWF; <https://www.ecmwf.int/>) and Copernicus's Data Stores. Allows for easy downloads of weather forecasts and climate reanalysis data in R. Data stores covered include the Climate Data Store (CDS; <https://cds.climate.copernicus.eu>), Atmosphere Data Store (ADS; <https://ads.atmosphere.copernicus.eu>) and Early Warning Data Store (CEMS; <https://ewds.climate.copernicus.eu>).