Computes the test statistics for examining the significance of autocorrelation in univariate time series, cross-correlation in bivariate time series, Pearson correlations in multivariate series and test statistics for i.i.d. property of univariate series given in Dalla, Giraitis and Phillips (2022), <https://www.cambridge.org/core/journals/econometric-theory/article/abs/robust-tests-for-white-noise-and-crosscorrelation/4D77C12C52433F4C6735E584C779403A>, <https://elischolar.library.yale.edu/cowles-discussion-paper-series/57/>.

Complete work flow for the analysis of pharmacokinetic pharmacodynamic (PKPD), physiologically-based pharmacokinetic (PBPK) and systems pharmacology models including: creation of ordinary differential equation-based models, pooled parameter estimation, individual/population based simulations, rule-based simulations for clinical trial design and modeling assays, deployment with a customizable Shiny app, and non-compartmental analysis. System-specific analysis templates can be generated and each element includes integrated reporting with PowerPoint and Word'.

Perform the analysis of the World Health Organization (WHO) Pharmacovigilance database VigiBase (Extract Case Level version), <https://who-umc.org/> e.g., load data, perform data management, disproportionality analysis, and descriptive statistics. Intended for pharmacovigilance routine use or studies. This package is NOT supported nor reflect the opinion of the WHO, or the Uppsala Monitoring Centre. Disproportionality methods are described by Norén et al (2013) <doi:10.1177/0962280211403604>.

Offers a suite of tools designed to enhance the responsiveness and interactivity of web-based documents and applications created with R. It provides an automatic, configurable resizing toolbar that can be seamlessly integrated with HTML elements such as containers, images, and tables, allowing end-users to dynamically adjust their dimensions. Beyond the toolbar, the package includes a rich collection of flexible, expandable, and interactive container functionalities, such as highly customizable split-screen layouts (splitCard), versatile sizeable cards (sizeableCard), dynamic window-like elements (windowCard), visually engaging emphasis cards (empahsisCard), and sophisticated flexible and elastic card layouts (flexCard, elastiCard). Furthermore, it offers an elegant image viewer and resizer (shinyExpandImage) perfect for interactive galleries. r2resize is particularly well-suited for developers and data scientists looking to create modern, responsive, and user-friendly shiny applications, markdown reports, and quarto documents that adapt gracefully to different screen sizes and user preferences, significantly improving the user experience.

This package produces tables with the level of replication (number of replicates) and the experimental uncoded values of the quantitative factors to be used for rotatable Central Composite Design (CCD) experimentation and a 2-D contour plot of the corresponding variance of the predicted response according to Mead et al. (2012) <doi:10.1017/CBO9781139020879> design_ccd(), and analyzes CCD data with response surface methodology ccd_analysis(). A rotatable CCD provides values of the variance of the predicted response that are concentrically distributed around the average treatment combination used in the experimentation, which with uniform precision (implied by the use of several replicates at the average treatment combination) improves greatly the search and finding of an optimum response. These properties of a rotatable CCD represent undeniable advantages over the classical factorial design, as discussed by Panneton et al. (1999) <doi:10.13031/2013.13267> and Mead et al. (2012) <doi:10.1017/CBO9781139020879.018> among others.

Monocle 3 performs clustering, differential expression and trajectory analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle 3 also performs differential expression analysis, clustering, visualization, and other useful tasks on single-cell expression data. It is designed to work with RNA-Seq data, but could be used with other types as well.

Tree based algorithms can be improved by introducing boosting frameworks. LightGBM is one such framework, based on Ke, Guolin et al. (2017). This package offers an R interface to work with it. It is designed to be distributed and efficient with the following goals:

1. Faster training speed and higher efficiency;

2. lower memory usage;

3. better accuracy;

4. parallel learning supported; and

5. capable of handling large-scale data.

This package is a collection of baseline correction algorithms. Beside those it provides a framework and a Tcl/Tk enabled GUI for optimizing baseline algorithm parameters. Typical use is the removal of the background effects from spectra, which are originating from various types of spectroscopy and spectrometry. Also, there is a possibility of optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more.

This package aims to make it easy to use various types of fonts (TrueType, OpenType, Type 1, web fonts, etc.) in R graphs, and supports most output formats of R graphics including PNG, PDF and SVG. Text glyphs will be converted into polygons or raster images, hence after the plot has been created, it no longer relies on the font files. No external software such as Ghostscript is needed to use this package.

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.

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 to analyse human and mosquito behavioral interactions and to compute exposure to mosquito bites estimates. Using behavioral data for human individuals and biting patterns for mosquitoes, you will be able to compute hourly exposure for bed net users and non-users, and summarize (e.g. proportion indoors and outdoors, proportion per time periods, and proportion prevented by bed nets) or visualize these dynamics across a 24-hour cycle.

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.

Scientific journal numeric formatting policies implemented in code. Emphasis on formatting mean/upper/lower sets of values to pasteable text for journal submission. For example c(2e6, 1e6, 3e6) becomes "2.00 million (1.00--3.00)". Lancet and Nature have built-in styles for rounding and punctuation marks. Users may extend journal styles arbitrarily. Four metrics are supported; proportions, percentage points, counts and rates. Magnitudes for all metrics are discovered automatically.

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.

Estimates group transmission assortativity coefficients from transmission trees. Group transmission assortativity coefficients measure the tendency for individuals to transmit within their own group (e.g. age group, vaccination status, or location) compared to other groups. The package requires information on who infected whom, group membership for all individuals, and the relative sizes of each group in the population. For more details see Geismar et al. (2024) <doi:10.1371/journal.pone.0313037>.

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>.

This package provides functions are provided for internal use by the spatial capture-recapture package secr (from version 5.4.0). The idea is to speed up the installation of secr', and possibly reduce its size. Initially the functions are those for area and transect search that use numerical integration code from RcppNumerical and RcppEigen'. The functions are not intended to be user-friendly and require considerable preprocessing of data.

Fit a univariate-guided sparse regression (lasso), by a two-stage procedure. The first stage fits p separate univariate models to the response. The second stage gives more weight to the more important univariate features, and preserves their signs. Conveniently, it returns an objects that inherits from class glmnet', so that all of the methods for glmnet are available. See Chatterjee, Hastie and Tibshirani (2025) <doi:10.1162/99608f92.c79ff6db> for details.

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'.

For distributions whose probability density functions are log-concave, the adaptive rejection sampling algorithm can be used to build envelope functions for sampling. For others, the modified adaptive rejection sampling algorithm, the concave-convex adaptive rejection sampling algorithm, and the adaptive slice sampling algorithm can be used. This R package mainly includes these four functions: rARS(), rMARS(), rCCARS(), and rASS(). These functions can realize sampling based on the algorithms above.