Efficiently implements the Graphical Lasso algorithm, utilizing the Armadillo C++ library for rapid computation. This algorithm introduces an L1 penalty to derive sparse inverse covariance matrices from observations of multivariate normal distributions. Features include the generation of random and structured sparse covariance matrices, beneficial for simulations, statistical method testing, and educational purposes in graphical modeling. A unique function for regularization parameter selection based on predefined sparsity levels is also offered, catering to users with specific sparsity requirements in their models. The methodology for sparse inverse covariance estimation implemented in this package is based on the work of Friedman, Hastie, and Tibshirani (2008) <doi:10.1093/biostatistics/kxm045>.

An R package for creating panels of diagnostic plots for residuals from a model using ggplot2 and plotly to analyze residuals and model assumptions from a variety of viewpoints. It also allows for the creation of interactive diagnostic plots.

Extra geoms and scales for ggplot2', including geom_cloud(), a Normal density cloud replacement for errorbars; transforms ssqrt_trans and pseudolog10_trans, which are loglike but appropriate for negative data; interp_trans() and warp_trans() which provide scale transforms based on interpolation; and an infix compose operator for scale transforms.

This package creates tables suitable for regulatory agency submission by leveraging the gtsummary package as the back end. Tables can be exported to HTML, Word, PDF and more. Highly customized outputs are available by utilizing existing styling functions from gtsummary as well as custom options designed for regulatory tables.

This package implements general unilateral loading estimator for two-layer latent factor models with smooth, element-wise factor transformations. We provide data simulation, loading estimation,finite-sample error bounds, and diagnostic tools for zero-mean and sub-Gaussian assumptions. A unified interface is given for evaluating estimation accuracy and cosine similarity. The philosophy of the package is described in Guo G. (2026) <doi:10.1016/j.apm.2025.116280>.

Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. For a single-cell dataset, the user can calculate the GTE value for individual features (such as genes), and then identify the highly batch-sensitive features. Removing these highly batch-sensitive features results in datasets with low batch effects.

This package contains five functions performing the calculation of unconditional and conditional Granger-causality spectra, bootstrap inference on both, and inference on the difference between them via the bootstrap approach of Farne and Montanari, 2018 <arXiv:1803.00374>.

Fit linear mixed-effects models using restricted (or residual) maximum likelihood (REML) and with generalized inverse matrices to specify covariance structures for random effects. In particular, the package is suited to fit quantitative genetic mixed models, often referred to as animal models'. Implements the average information algorithm as the main tool to maximize the restricted log-likelihood, but with other algorithms available.

This package provides a suite of tools for specifying and examining experimental designs related to choice response time models (e.g., the Diffusion Decision Model). This package allows users to define how experimental factors influence one or more model parameters using R-style formula syntax, while also checking the logical consistency of these associations. Additionally, it integrates with the ggdmc package, which employs Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampling to optimise model parameters. For further details on the model-building approach, see Heathcote, Lin, Reynolds, Strickland, Gretton, and Matzke (2019) <doi:10.3758/s13428-018-1067-y>.

This small collection of functions provides what we call elemental graphics for display of analysis of variance results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) <doi:10.2307/2684513>, Robert M. Pruzek and James E. Helmreich <https://jse.amstat.org/v17n1/helmreich.html>. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular analysis of variance methods. These functions can be particularly helpful for students and non-statistician analysts. But these methods should be quite generally helpful for work-a-day applications of all kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data.

The main purpose of this package is to allow fitting of mixture distributions with generalised additive models for location scale and shape models see Chapter 7 of Stasinopoulos et al. (2017) <doi:10.1201/b21973-4>.

This is a companion to Henry-Stewart talk by Zhao (2026, <doi:10.69645/FRFQ9519>), which gathers information, metadata and scripts to showcase modern genetic analysis -- ranging from testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with traits using genetic and statistical models, Bayesian implementation, power calculation in study design, and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications. The earlier version by Zhao (2009, <doi:10.69645/DCRY5578>) provides an brief introduction to these topics.

Efficiently manage and process data from oTree experiments. Import oTree data and clean them by using functions that handle messy data, dropouts, and other problematic cases. Create IDs, calculate the time, transfer variables between app data frames, and delete sensitive information. Review your experimental data prior to running the experiment and automatically generate a detailed summary of the variables used in your oTree code. Information on oTree is found in Chen, D. L., Schonger, M., & Wickens, C. (2016) <doi:10.1016/j.jbef.2015.12.001>.

This package provides functions for simulating and estimating parameters of various growth models, including Logistic, Exponential, Theta-logistic, Von-Bertalanffy, and Gompertz models. The package supports both simulated and real data analysis, including parameter estimation, visualization, and calculation of global and local estimates. The methods are based on research described by Md Aktar Ul Karim and Amiya Ranjan Bhowmick (2022) in (<https://www.researchsquare.com/article/rs-2363586/v1>). An interactive web application is also available at [GPEMR Web App](<https://gpem-r.shinyapps.io/GPEM-R/>).

The ggplot2 package is the state-of-the-art toolbox for creating and formatting graphs. However, it is easy to forget how certain formatting commands are named and sometimes users find themselves asking: How do you rotate the x-axis labels again? Or how do you hide the legend...? This package allows users to issue natural language commands related to theme-related styling of plots (colors, font size and such), which then are translated into valid ggplot2 commands.

Trace plots and convergence diagnostics for Markov Chain Monte Carlo (MCMC) algorithms on highly multivariate or unordered spaces. Methods outlined in a forthcoming paper.

Derivative Free Gradient Projection Algorithms for Factor Rotation. For more details see ?GPArotateDF. Theory for these functions can be found in the following publications: Jennrich (2004) <doi:10.1007/BF02295647>. Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.

The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.

Add vector field layers to ggplots. Ideal for visualising wind speeds, water currents, electric/magnetic fields, etc. Accepts data.frames, simple features (sf), and spatiotemporal arrays (stars) objects as input. Vector fields are depicted as arrows starting at specified locations, and with specified angles and radii.

Easy wrangling and model-free analysis of microbial growth curve data, as commonly output by plate readers. Tools for reshaping common plate reader outputs into tidy formats and merging them with design information, making data easy to work with using gcplyr and other packages. Also streamlines common growth curve processing steps, like smoothing and calculating derivatives, and facilitates model-free characterization and analysis of growth data. See methods at <https://mikeblazanin.github.io/gcplyr/>.

Probability propagation in Bayesian networks, also known as graphical independence networks. Documentation of the package is provided in vignettes included in the package and in the paper by Højsgaard (2012, <doi:10.18637/jss.v046.i10>). See citation("gRain") for details.

The Gaussian Interval Plot (GIplot) is a pictorial representation of the mean and the standard deviation of a quantitative variable. It also flags potential outliers (together with their frequencies) that are c standard deviations away from the mean.

This package provides a simple and intuitive high-level language for music representation. Generates and embeds music scores and audio files in RStudio', R Markdown documents, and R Jupyter Notebooks'. Internally, uses MusicXML <https://github.com/w3c/musicxml> to represent music, and MuseScore <https://musescore.org/> to convert MusicXML'.

This package provides methods to calculate sensitivities of financial option prices for European, geometric and arithmetic Asian, and American options, with various payoff functions in the Black Scholes model, and in more general jump diffusion models. A shiny app to interactively plot the results is included. Furthermore, methods to compute implied volatilities are provided for a wide range of option types and custom payoff functions. Classical formulas are implemented for European options in the Black Scholes Model, as is presented in Hull, J. C. (2017), Options, Futures, and Other Derivatives. In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see Hudde, A. & Rüschendorf, L. (2023). European and Asian Greeks for exponential Lévy processes. <doi:10.1007/s11009-023-10014-5>. For American options, the Binomial Tree Method is implemented, as is presented in Hull, J. C. (2017).