This package provides a suite of non-parametric, visual tools for assessing differences in data structures for two datasets that contain different observations of the same variables. These tools are all based on Principal Component Analysis (PCA) and thus effectively address differences in the structures of the covariance matrices of the two datasets. The PCASDC tools consist of easy-to-use, intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The angle plot presents the information loss when moving from the PCA decomposition of one dataset to the PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby presenting the relative weighting and importance of the variables from the original dataset.

This package provides functions to make board game graphics with the ggplot2', grid', rayrender', rayvertex', and rgl packages. Specializes in game diagrams, animations, and "Print & Play" layouts for the piecepack <https://www.ludism.org/ppwiki> but can make graphics for other board game systems. Includes configurations for several public domain game systems such as checkers, (double-18) dominoes, go, piecepack', playing cards, etc.

This software has evolved from fisheries research conducted at the Pacific Biological Station (PBS) in Nanaimo', British Columbia, Canada. It extends the R language to include two-dimensional plotting features similar to those commonly available in a Geographic Information System (GIS). Embedded C code speeds algorithms from computational geometry, such as finding polygons that contain specified point events or converting between longitude-latitude and Universal Transverse Mercator (UTM) coordinates. Additionally, we include C++ code developed by Angus Johnson for the Clipper library, data for a global shoreline, and other data sets in the public domain. Under the user's R library directory .libPaths()', specifically in ./PBSmapping/doc', a complete user's guide is offered and should be consulted to use package functions effectively.

This package provides a high performance package implementing random effects and/or sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2023) <doi:10.2139/ssrn.2702053>.

Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model.

The population proportion using group testing can be estimated by different methods. Four functions including p.mle(), p.gart(), p.burrow() and p.order() are provided to implement four estimating methods including the maximum likelihood estimate, Gart's estimate, Burrow's estimate, and order statistic estimate.

This package contains functions to simulate the most commonly used SAS® procedures. Specifically, the package aims to simulate the functionality of proc freq', proc means', proc ttest', proc reg', proc transpose', proc sort', and proc print'. The simulation will include recreating all statistics with the highest fidelity possible.

This package provides a simple way to add page numbers to base/ggplot/lattice graphics.

It is often useful when developing an R package to track the relationship between functions in order to appropriately test and track changes. This package generates a graph of the relationship between all R functions in a package. It can also be used on any directory containing .R files which can be very useful for shiny apps or other non-package workflows.

Easy and efficient access to the API provided by Prevedere', an industry insights and predictive analytics company. Query and download indicators, models and workbenches built with Prevedere for further analysis and reporting <https://www.prevedere.com/>.

Utilize the Bayesian prior and posterior predictive checking approach to provide a statistical assessment of replication success and failure. The package is based on the methods proposed in Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.

This package implements projection pursuit forest algorithm for supervised classification.

Defines functions to describe regression models using only pre-computed summary statistics (i.e. means, variances, and covariances) in place of individual participant data. Possible models include linear models for linear combinations, products, and logical combinations of phenotypes. Implements methods presented in Wolf et al. (2021) <doi:10.3389/fgene.2021.745901> Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.

This package provides a user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

Optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) <doi:10.1016/S0169-2607(03)00073-7>.

Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) <doi:10.48550/arXiv.2504.00423>.

Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified variables (e.g. start of a month).

Text mining of PubMed Abstracts (text and XML) from <https://pubmed.ncbi.nlm.nih.gov/>.

This package provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly. It is built upon ggplot2 and other plotting packages, and is designed to be easy to use and to work seamlessly with ggplot2 objects. It is particularly useful for creating complex plots with multiple layers, facets, and annotations. It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees. The package is designed to be flexible and customizable, and to work well with the ggplot2 ecosystem. The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.

Because larger (> 50 MB) data files cannot easily be committed to git, a different approach is required to manage data associated with an analysis in a GitHub repository. This package provides a simple work-around by allowing larger (up to 2 GB) data files to piggyback on a repository as assets attached to individual GitHub releases. These files are not handled by git in any way, but instead are uploaded, downloaded, or edited directly by calls through the GitHub API. These data files can be versioned manually by creating different releases. This approach works equally well with public or private repositories. Data can be uploaded and downloaded programmatically from scripts. No authentication is required to download data from public repositories.

This package provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par().

Evaluate a function across a grid of parameters. The function may be evaluated once, or many times for simulation. Parallel computing is facilitated. Utilities aim at performing analyses of power and sample size, allowing for easy search of minimum n (or min/max of any other parameter) to achieve a desired minimal level of power (or maximum of any other objective). Plotting functions are included that present the dependency of n and power in relation to further assumptions.

An interactive document on the topic of basic probability using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/BayesShiny/>.

An embedded proximal interior point quadratic programming solver, which can solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and Jones (2023) <doi:10.48550/arXiv.2304.00290>. Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex quadratic programming problems without the need for linear independence of the constraints. The solver is written in header only C++ 14 leveraging the Eigen library for vectorized linear algebra. For small dense problems, vectorized instructions and cache locality can be exploited more efficiently. Allocation free problem updates and re-solves are also provided.