Interactive data tables for R, based on the React Table JavaScript library. Provides an HTML widget that can be used in R Markdown or Quarto documents, Shiny applications, or viewed from an R console.

Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of distributions, also known as the generalized error distribution. Estimation is made using the design of Bottazzi (2004) <https://ideas.repec.org/p/ssa/lemwps/2004-14.html>, where the likelihood is maximized by several optimization procedures using the GNU Scientific Library (GSL)', translated to C++ code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric Laplace distributions.

When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question.

Defines the underlying pipeline structure for reproducible neuroscience, adopted by RAVE (reproducible analysis and visualization of intracranial electroencephalography); provides high-level class definition to build, compile, set, execute, and share analysis pipelines. Both R and Python are supported, with Markdown and shiny dashboard templates for extending and building customized pipelines. See the full documentations at <https://rave.wiki>; to cite us, check out our paper by Magnotti, Wang, and Beauchamp (2020, <doi:10.1016/j.neuroimage.2020.117341>), or run citation("ravepipeline") for details.

Duplicated restaurant data (pre-processed and formatted) for entity resolution. This package contains formatted data from a data set that contains information about different restaurants, with the Zagats portion containing 331 records and the Fodors portion containing 533 records. The following variables are included in the data set: id, name, address, city, phone, type. The data set has a respective gold data set that provides information on which records match based on id.

Generates disease-specific drug-response profiles that are independent of time, concentration, and cell-line. Based on the cell lines used as surrogates, the returned profiles represent the unique transcriptional changes induced by a compound in a given disease.

This package provides a part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop.

Gather boxscore, play-by-play, and auxiliary data from Major League Volleyball (MLV) <https://provolleyball.com>, League One Volleyball Pro (LOVB) <https://www.lovb.com/pro-league>, and Athletes Unlimited Pro Volleyball (AU) <https://auprosports.com/volleyball/> to create a repository of basic and advanced statistics for teams and players.

An implementation of Bayesian model-averaged t-tests that allows users to draw inferences about the presence versus absence of an effect, variance heterogeneity, and potential outliers. The RoBTT package estimates ensembles of models created by combining competing hypotheses and applies Bayesian model averaging using posterior model probabilities. Users can obtain model-averaged posterior distributions and inclusion Bayes factors, accounting for uncertainty in the data-generating process (Maier et al., 2024, <doi:10.3758/s13423-024-02590-5>). The package also provides a truncated likelihood version of the model-averaged t-test, enabling users to exclude potential outliers without introducing bias (Godmann et al., 2024, <doi:10.31234/osf.io/j9f3s>). Users can specify a wide range of informative priors for all parameters of interest. The package offers convenient functions for summary, visualization, and fit diagnostics.

Create an R Journal Rmarkdown template article, that will generate html and pdf versions of your paper. Check that the paper folder has all the required components needed for submission. Examples of R Journal publications can be found at <https://journal.r-project.org>.

This package provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis. A good reference on these methods is Chapter 10 of Wu, C-F J and Hamada, M (2009) "Experiments: Planning, Analysis, and Parameter Design Optimization" ISBN 978-0-471-69946-0. An early version of the package is documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.

Adds a user-facing undo and redo history to Shiny applications. Application state, comprising registered inputs and optionally server-side reactive values, is captured as the user interacts with the application. Users may then step backwards and forwards through that history with the keyboard, with buttons, or by scrubbing a visual history rail. Rapid successive changes, such as dragging a slider, are coalesced into a single history entry, and related changes may be grouped explicitly into semantic steps.

Native R interface to TMB (Template Model Builder) so models can be written entirely in R rather than C++'. Automatic differentiation, to any order, is available for a rich subset of R features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. RTMB provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

This package implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.

R-based access to mass-spectrometry (MS) data. While many packages exist to process MS data, many of these make it difficult to access the underlying mass-to-charge ratio (m/z), intensity, and retention time of the files themselves. This package is designed to format MS data in a tidy fashion and allows the user perform the plotting and analysis.

This package provides a flexible framework for implementing hierarchical access control in shiny applications. Features include user permission management through a two-tier system of access panels and units, pluggable shiny module for administrative interfaces, and support for multiple storage backends (local, AWS S3', Posit Connect'). The system enables fine-grained control over application features, with built-in audit trails and user management capabilities. Integrates seamlessly with Posit Connect's authentication system.

Analyze multi-level one-way experimental designs where there are unequal sample sizes and population variance homogeneity can not be assumed. To conduct the Gabriel test <doi:10.2307/2286265>, create two vectors: one for your observations and one for the factor level of each observation. The function, rgabriel, conduct the test and save the output as a vector to input into the gabriel.plot function, which produces a confidence interval plot for Multiple Comparison.

Queries data from WHOIS servers.

Cloth Simulation Filter (CSF) is an airborne LiDAR (Light Detection and Ranging) ground points filtering algorithm which is based on cloth simulation. It tries to simulate the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface <https://www.mdpi.com/2072-4292/8/6/501/htm>.

Implementation of the relative placement algorithm widely used in the scoring of Lindy Hop and West Coast Swing dance contests.

An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Hoffman *et al.* (1999) (<doi:10.1145/331770.331775>) for original implementation, see Di Caro *et al* (2012) (<doi:10.1007/978-3-642-13672-6_13>), for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.

An R command interface to the MLwiN multilevel modelling software package.

This package provides a collection of small text corpora of interesting data. It contains all data sets from dariusk/corpora'. Some examples: names of animals: birds, dinosaurs, dogs; foods: beer categories, pizza toppings; geography: English towns, rivers, oceans; humans: authors, US presidents, occupations; science: elements, planets; words: adjectives, verbs, proverbs, US president quotes.

Automatic coding of open-ended responses to the Cognitive Reflection Test (CRT), a widely used class of tests in cognitive science and psychology that assess the tendency to override an initial intuitive (but incorrect) answer and engage in reflection to reach a correct solution. The package standardizes CRT response coding across datasets in cognitive psychology, decision-making, and related fields. Automated coding reduces manual effort and improves reproducibility by limiting variability from subjective interpretation of open-ended responses. The package supports automatic coding and machine scoring for the original English-language CRT (Frederick, 2005) <doi:10.1257/089533005775196732>, CRT4 and CRT7 (Toplak et al., 2014) <doi:10.1080/13546783.2013.844729>, CRT-long (Primi et al., 2016) <doi:10.1002/bdm.1883>, and CRT-2 (Thomson & Oppenheimer, 2016) <doi:10.1017/s1930297500007622>.