R Commander plugin for teaching statistical methods. It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.

Data objects in R can be rendered as HTML tables using the JavaScript library ag-grid (typically via R Markdown or Shiny'). The ag-grid library has been included in this R package. The package name RagGrid is an abbreviation of R agGrid'.

This package provides tools for testing differential item functioning (DIF) and differential test functioning (DTF) in two-group item response theory models. The package estimates robust scaling parameters via iteratively reweighted least squares with Tukey's bisquare loss, and supports Wald-type tests of item-level and test-level differences from robust scaling parameters. Inputs can be supplied directly from model parameter/covariance objects or extracted from fitted mirt and lavaan models. Methods are described in Halpin (2022) <doi:10.48550/arXiv.2207.04598>.

This package provides functions for assessing variable relations and associations prior to modeling with a Random Forest algorithm (although these are relevant for any predictive model). Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) <https://digitalcommons.usu.edu/etd/8587/>.

This package implements a unified framework combining staggered difference-in-differences with regression discontinuity designs and network interference. Extends Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001> to settings where treatment assignment is determined by a running variable crossing a cutoff, adoption timing is heterogeneous across units, and spillover effects operate through a known network structure. Provides group-time average treatment effects (direct and spillover), aggregation schemes, bandwidth selection, and pre-treatment falsification tests.

This package provides a thin wrapper around the tiktoken-rs crate, allowing to encode text into Byte-Pair-Encoding (BPE) tokens and decode tokens back to text. This is useful to understand how Large Language Models (LLMs) perceive text.

R functions for generating and/or displaying random Chuck Norris facts. Based on data from the Internet Chuck Norris database ('ICNDb').

This package provides a framework for estimating ensembles of parametric survival models with different parametric families. The RoBSA framework uses Bayesian model-averaging to combine the competing parametric survival models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual predictors or preference for a parametric family (BartoÅ¡, Aust & Haaf, 2022, <doi:10.1186/s12874-022-01676-9>). The user can define a wide range of informative priors for all parameters of interest. The package provides convenient functions for summary, visualizations, fit diagnostics, and prior distribution calibration.

This holds r markdown and quarto templates for academic papers and slide decks. It also has templates to create research projects which contain academic papers as vignettes.

Create rich and fully interactive 3D visualizations of molecular data. Visualizations can be included in Shiny apps and R markdown documents, or viewed from the R console and RStudio Viewer. r3dmol includes an extensive API to manipulate the visualization after creation, and supports getting data out of the visualization into R. Based on the 3dmol.js and the htmlwidgets R package.

The minimum covariance determinant estimator is used to perform robust quadratic discriminant analysis, including cross-validation. References: Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>.

This package provides a model of single-layer groundwater flow in steady-state under the Dupuit-Forchheimer assumption can be created by placing elements such as wells, area-sinks and line-sinks at arbitrary locations in the flow field. Output variables include hydraulic head and the discharge vector. Particle traces can be computed numerically in three dimensions. The underlying theory is described in Haitjema (1995) <doi:10.1016/B978-0-12-316550-3.X5000-4> and references therein.

This package provides several non parametric randomness tests for numeric sequences.

This package provides a flexible and streamlined pipeline for formatting, analyzing, and visualizing omics data, regardless of omics type (e.g. transcriptomics, proteomics, metabolomics). The package includes tools for shaping input data into analysis-ready structures, fitting linear or mixed-effect models, extracting key contrasts, and generating a rich variety of ready-to-use publication-quality plots. Designed for transparency and reproducibility across a wide range of study designs, with customizable components for statistical modeling.

This package provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning. C. Zhang, Y. Liu, J. Wang and H. Zhu. (2016) <doi:10.1080/10618600.2015.1043010>.

Scelestial infers a lineage tree from single-cell DNA mutation matrix. It generates a tree with approximately maximum parsimony through a Steiner tree approximation algorithm.

Portfolio optimization is achieved through a combination of regularization techniques and ensemble methods that are designed to generate stable out-of-sample return predictions, particularly in the presence of strong correlations among assets. The package includes functions for data preparation, parallel processing, and portfolio analysis using methods such as Mean-Variance, James-Stein, LASSO, Ridge Regression, and Equal Weighting. It also provides visualization tools and performance metrics, such as the Sharpe ratio, volatility, and maximum drawdown, to assess the results.

Turns nested lists into data.frames in an orderly manner.

Recursive display of names and paths of all the items nested within sublists of a list object.

This package provides a convenient way to read fixed-width ASCII polling datasets from providers like the Roper Center <https://ropercenter.cornell.edu>.

Flexible rounding functions for use in error detection. They were outsourced from the scrutiny package.

Allows the user to view an image in full screen when clicking on it in RMarkdown documents and shiny applications. The package relies on the JavaScript library intense-images'. See <https://tholman.com/intense-images/> for more information.

Generate utils::globalVariables() from roxygen2 @global and @autoglobal tags.

This package provides an interface to the Vamp audio analysis plugin system <https://www.vamp-plugins.org/> developed by Queen Mary University of London's Centre for Digital Music. Enables loading and running Vamp plugins for various audio analysis tasks including tempo detection, onset detection, spectral analysis, and audio feature extraction. Supports mono and stereo audio with automatic channel adaptation and domain conversion.