This package implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.

This is a set of tools for dendrograms and tree plots using ggplot2. The ggplot2 philosophy is to clearly separate data from the presentation. Unfortunately the plot method for dendrograms plots directly to a plot device with out exposing the data. The ggdendro package resolves this by making available functions that extract the dendrogram plot data. The package provides implementations for tree, rpart, as well as diana and agnes cluster diagrams.

This is an R package for spell checking common document formats including LaTeX, markdown, manual pages, and DESCRIPTION files. It includes utilities to automate checking of documentation and vignettes as a unit test during R CMD check. Both British and American English are supported out of the box and other languages can be added. In addition, packages may define a wordlist to allow custom terminology without having to abuse punctuation.

This package provides p-values in type I, II or III anova and summary tables for lmer model fits via Satterthwaite's degrees of freedom method. A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available.

This package implements a simple key-value style database where character string keys are associated with data values that are stored on the disk. A simple interface is provided for inserting, retrieving, and deleting data from the database. Utilities are provided that allow filehash databases to be treated much like environments and lists are already used in R. These utilities are provided to encourage interactive and exploratory analysis on large datasets.

Winit is a window creation and management library. It can create windows and lets you handle events (for example: the window being resized, a key being pressed, a mouse movement, etc.) produced by window.

Winit is designed to be a low-level brick in a hierarchy of libraries. Consequently, in order to show something on the window you need to use the platform-specific getters provided by winit, or another library.

Winit is a window creation and management library. It can create windows and lets you handle events (for example: the window being resized, a key being pressed, a mouse movement, etc.) produced by window.

Winit is designed to be a low-level brick in a hierarchy of libraries. Consequently, in order to show something on the window you need to use the platform-specific getters provided by winit, or another library.

Winit is a window creation and management library. It can create windows and lets you handle events (for example: the window being resized, a key being pressed, a mouse movement, etc.) produced by window.

Winit is designed to be a low-level brick in a hierarchy of libraries. Consequently, in order to show something on the window you need to use the platform-specific getters provided by winit, or another library.

Winit is a window creation and management library. It can create windows and lets you handle events (for example: the window being resized, a key being pressed, a mouse movement, etc.) produced by window.

Winit is designed to be a low-level brick in a hierarchy of libraries. Consequently, in order to show something on the window you need to use the platform-specific getters provided by winit, or another library.

Winit is a window creation and management library. It can create windows and lets you handle events (for example: the window being resized, a key being pressed, a mouse movement, etc.) produced by window.

Winit is designed to be a low-level brick in a hierarchy of libraries. Consequently, in order to show something on the window you need to use the platform-specific getters provided by winit, or another library.

Winit is a window creation and management library. It can create windows and lets you handle events (for example: the window being resized, a key being pressed, a mouse movement, etc.) produced by window.

Winit is designed to be a low-level brick in a hierarchy of libraries. Consequently, in order to show something on the window you need to use the platform-specific getters provided by winit, or another library.

Winit is a window creation and management library. It can create windows and lets you handle events (for example: the window being resized, a key being pressed, a mouse movement, etc.) produced by window.

Winit is designed to be a low-level brick in a hierarchy of libraries. Consequently, in order to show something on the window you need to use the platform-specific getters provided by winit, or another library.

This Rust crate implements a file system walk that runs in parallel using rayon. It attempts to combine the parallelism of ignore with walkdir's streaming iterator API. Entries are streamed in sorted order with options for custom sorting, filtering, and skipping.

Directory traversal is already pretty fast. If you don't need this crate's speed then walkdir provides a smaller and more tested single threaded implementation.

This package provides a backward-pipe operator for magrittr (%<%) or pipeR (%<<%) that allows for a performing operations from right-to-left. This allows writing more legible code where right-to-left ordering is natural. This is common with hierarchies and nested structures such as trees, directories or markup languages (e.g. HTML and XML). The package also includes a R-Studio add-in that can be bound to a keyboard shortcut.

This package provides a method for identifying pattern changes between 2 experimental conditions in correlation networks (e.g., gene co-expression networks), which builds on a commonly used association measure, such as Pearson's correlation coefficient. This package includes functions to calculate correlation matrices for high-dimensional dataset and to test differential correlation, which means the changes in the correlation relationship among variables (e.g., genes and metabolites) between 2 experimental conditions.

This package provides tools for flexible non-linear least squares model fitting using general-purpose optimization techniques. The package supports a variety of optimization algorithms, including those provided by the optimx package, making it suitable for handling complex non-linear models. Features include parallel processing support via the future and foreach packages, comprehensive model diagnostics, and visualization capabilities. Implements methods described in Nash and Varadhan (2011, <doi:10.18637/jss.v043.i09>).

Graphical approach provides a useful framework for multiplicity adjustment in clinical trials with multiple endpoints. This package includes statistical methods to optimize sample size over initial weight and transition probability in a graphical approach under a common setting, which is to use marginal power for each endpoint in a trial design. See Zhang, F. and Gou, J. (2023). Sample size optimization for clinical trials using graphical approaches for multiplicity adjustment, Technical Report.

Changes of landscape diversity and structure can be detected soon if relying on landscape class combinations and analysing patterns at multiple scales. LandComp provides such an opportunity, based on Juhász-Nagy's functions (Juhász-Nagy P, Podani J 1983 <doi:10.1007/BF00129432>). Functions can handle multilayered data. Requirements of the input: binary data contained by a regular square or hexagonal grid, and the grid should have projected coordinates.

This package provides a toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.

Publicly available data from Medicare frequently requires extensive initial effort to extract desired variables and merge them; this package formalizes the techniques I've found work best. More information on the Medicare program, as well as guidance for the publicly available data this package targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.

The Vega-Lite JavaScript framework provides a higher-level grammar for visual analysis, akin to ggplot or Tableau', that generates complete Vega specifications. Functions exist which enable building a valid spec from scratch or importing a previously created spec file. Functions also exist to export spec files and to generate code which will enable plots to be embedded in properly configured web pages. The default behavior is to generate an htmlwidget'.

cfDNA fragments carry important features for building cancer sample classification ML models, such as fragment size, and fragment end motif etc. Analyzing and visualizing fragment size metrics, as well as other biological features in a curated, standardized, scalable, well-documented, and reproducible way might be time intensive. This package intends to resolve these problems and simplify the process. It offers two sets of functions for cfDNA feature characterization and visualization.

Finding an optimal Bayesian experimental design involves maximizing an objective function given by the expectation of some appropriately chosen utility function with respect to the joint distribution of unknown quantities (including responses). This objective function is usually not available in closed form and the design space can be continuous and of high dimensionality. This package uses Approximate Coordinate Exchange (ACE) to maximise an approximation to the expectation of the utility function.

This package provides a Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (<https://www.nature.com/articles/s41598-018-28130-5 >).