Calculates concentration and dispersion in ordered rating scales. It implements various measures of concentration and dispersion to describe what researchers variably call agreement, concentration, consensus, dispersion, or polarization among respondents in ordered data. It also implements other related measures to classify distributions. In addition to a generic city-block based concentration measure and a generic dispersion measure, the package implements various measures, including van der Eijk's (2001) <DOI: 10.1023/A:1010374114305> measure of agreement A, measures of concentration by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, Reardon, and Garcia-Montalvo and Reynal-Querol. Furthermore, the package provides an implementation of Galtungs AJUS-system to classify distributions, as well as a function to identify the position of multiple modes.

Assists the evaluation of whether and where to focus code optimization, using Amdahl's law and visual aids based on line profiling. Amdahl's profiler organizes profiling output files (including memory profiling) in a visually appealing way. It is meant to help to balance development vs. execution time by helping to identify the most promising sections of code to optimize and projecting potential gains. The package is an addition to R's standard profiling tools and is not a wrapper for them.

Create data that displays generative art when mapped into a ggplot2 plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using ggplot2 for generative art.

Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.

This package provides a collection of datasets on the Alone survival TV series in tidy format. Included in the package are 4 datasets detailing the survivors, their loadouts, episode details and season information.

Retrieve Amazon EC2 instance metadata from within the running instance.

Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 <doi:10.1177/1094428119836486>). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 <doi:10.31234/osf.io/hmnrc>). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 <doi:10.1177/0013164410375112>) or minimize item location differences (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>) depending on scoring models. Decision of which items should be assigned to the same block, termed item pairing, is thus critical to the quality of an FC test. This pairing process is essentially an optimization process which is currently carried out manually. However, given that we often need to simultaneously meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per trait are relatively large. To address these problems, autoFC is developed as a practical tool for facilitating the automatic construction of FC tests (Li et al., 2022 <doi:10.1177/01466216211051726>), essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods. Given characteristics of each item (and item responses), FC measures can be constructed either automatically based on user-defined pairing criteria and weights, or based on exact specifications of each block (i.e., blueprint; see Li et al., 2024 <doi:10.1177/10944281241229784>). Users can also generate simulated responses based on the Thurstonian Item Response Theory model (Brown & Maydeu-Olivares, 2011 <doi:10.1177/0013164410375112>) and predict trait scores of simulated/actual respondents based on an estimated model.

Developed as an R alternative to the AeroEvap model developed by the Desert Research Institute (DRI) in python <https://github.com/WSWUP/AeroEvap/blob/master/README.rst> which estimates open water evaporation using the aerodynamic mass transfer approach.

Opens and imports log files from Angstrom Engineering Thermal Evaporator and extracts basic characteristics, such as base pressure, time of the evaporation. It can visualize the deposition observables for review.

Static code compilation of a shiny app given an R function (into ui.R and server.R files or into a shiny app object). See examples at <https://github.com/alekrutkowski/autoshiny>.

Designed for studies where animals tagged with acoustic tags are expected to move through receiver arrays. This package combines the advantages of automatic sorting and checking of animal movements with the possibility for user intervention on tags that deviate from expected behaviour. The three analysis functions (explore(), migration() and residency()) allow the users to analyse their data in a systematic way, making it easy to compare results from different studies. CJS calculations are based on Perry et al. (2012) <https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data>.

Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) <DOI:10.2139/ssrn.2823687>.

This package provides a user-friendly shiny application to explore statistical associations and visual patterns in multivariate datasets. The app provides interactive correlation networks, bivariate plots, and summary tables for different types of variables (numeric and categorical). It also supports optional survey weights and range-based filters on association strengths, making it suitable for the exploration of survey and public data by non-technical users, journalists, educators, and researchers. For background and methodological details, see Soetewey et al. (2025) <doi:10.1016/j.softx.2025.102483>.

Simple animated versions of basic R plots, using the animation package. Includes animated versions of plot, barplot, persp, contour, filled.contour, hist, curve, points, lines, text, symbols, segments, and arrows.

Plot stacked areas and confidence bands as filled polygons, or add polygons to existing plots. A variety of input formats are supported, including vectors, matrices, data frames, formulas, etc.

Comprehensive set of tools for performing system identification of both linear and nonlinear dynamical systems directly from data. The Automatic Regression for Governing Equations (ARGOS) simplifies the complex task of constructing mathematical models of dynamical systems from observed input and output data, supporting various types of systems, including those described by ordinary differential equations. It employs optimal numerical derivatives for enhanced accuracy and employs formal variable selection techniques to help identify the most relevant variables, thereby enabling the development of predictive models for system behavior analysis.

This package provides tools for downloading and extracting data from the Copernicus "Agrometeorological indicators from 1979 to present derived from reanalysis" <https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview> (AgERA5).

Defines an algebra over maximum likelihood estimators (MLEs) by providing operators that are closed over MLEs, along with various statistical functions for inference. For background on maximum likelihood estimation, see Casella and Berger (2002, ISBN:978-0534243128). For the delta method and variance estimation, see Lehmann and Casella (1998, ISBN:978-0387985022).

Allow user to run the Adaptive Correlated Spike and Slab (ACSS) algorithm, corresponding INdependent Spike and Slab (INSS) algorithm, and Giannone, Lenza and Primiceri (GLP) algorithm with adaptive burn-in. All of the three algorithms are used to fit high dimensional data set with either sparse structure, or dense structure with smaller contributions from all predictors. The state-of-the-art GLP algorithm is in Giannone, D., Lenza, M., & Primiceri, G. E. (2021, ISBN:978-92-899-4542-4) "Economic predictions with big data: The illusion of sparsity". The two new algorithms, ACSS algorithm and INSS algorithm, and the discussion on their performance can be seen in Yang, Z., Khare, K., & Michailidis, G. (2024, submitted to Journal of Business & Economic Statistics) "Bayesian methodology for adaptive sparsity and shrinkage in regression".

The meaning of adea is "alternate DEA". This package is devoted to provide the alternative method of DEA described in the paper entitled "Stepwise Selection of Variables in DEA Using Contribution Load", by F. Fernandez-Palacin, M. A. Lopez-Sanchez and M. Munoz-Marquez. Pesquisa Operacional 38 (1), pg. 1-24, 2018. <doi:10.1590/0101-7438.2018.038.01.0031>. A full functional on-line and interactive version is available at <https://knuth.uca.es/shiny/DEA/>.

To address the violation of the assumption of normally distributed variables, researchers frequently employ bootstrapping. Building upon established packages for R (Sigmann et al. (2024) <doi:10.32614/CRAN.package.afex>, Lenth (2024) <doi:10.32614/CRAN.package.emmeans>), we provide bootstrapping functions to approximate a normal distribution of the parameter estimates for between-subject, within-subject, and mixed one-way and two-way ANOVA.

Finds, prioritizes and deletes erroneous taxa in a phylogenetic tree. This package calculates scores for taxa in a tree. Higher score means the taxon is more erroneous. If the score is zero for a taxon, the taxon is not erroneous. This package also can remove all erroneous taxa automatically by iterating score calculation and pruning taxa with the highest score.

Utilities for working with hourly air quality monitoring data with a focus on small particulates (PM2.5). A compact data model is structured as a list with two dataframes. A meta dataframe contains spatial and measuring device metadata associated with deployments at known locations. A data dataframe contains a datetime column followed by columns of measurements associated with each "device-deployment". Algorithms to calculate NowCast and the associated Air Quality Index (AQI) are defined at the US Environmental Projection Agency AirNow program: <https://document.airnow.gov/technical-assistance-document-for-the-reporting-of-daily-air-quailty.pdf>.

Estimate and plot confounder-adjusted survival curves using either Direct Adjustment', Direct Adjustment with Pseudo-Values', various forms of Inverse Probability of Treatment Weighting', two forms of Augmented Inverse Probability of Treatment Weighting', Empirical Likelihood Estimation or Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times. Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) <doi:10.1002/sim.9681>.