This package provides a set of tools for data wrangling, spatial data analysis, statistical modeling (including direct, model-assisted, photo-based, and small area tools), and USDA Forest Service data base tools. These tools are aimed to help Foresters, Analysts, and Scientists extract and perform analyses on USDA Forest Service data.

This package provides a framework to perform soft clustering using simplex-structured matrix factorisation (SSMF). The package contains a set of functions for determining the optimal number of prototypes, the optimal algorithmic parameters, the estimation confidence intervals and the diversity of clusters. Abdolali, Maryam & Gillis, Nicolas (2020) <doi:10.1137/20M1354982>.

Bayesian reconstruction of who infected whom during past outbreaks using routinely-collected surveillance data. Inference of transmission trees using genotype, age specific social contacts, distance between cases and onset dates of the reported cases. (Robert A, Kucharski AJ, Gastanaduy PA, Paul P, Funk S. (2020) <doi:10.1098/rsif.2020.0084>).

It provides utility functions for investigating changes within R packages. The pkgInfo() function extracts package information such as exported and non-exported functions as well as their arguments. The pkgDiff() function compares this information for two versions of a package and creates a diff file viewable in a browser.

This package provides an interface to a Simplex Tree data structure, which is a data structure aimed at enabling efficient manipulation of simplicial complexes of any dimension. The Simplex Tree data structure was originally introduced by Jean-Daniel Boissonnat and Clément Maria (2014) <doi:10.1007/s00453-014-9887-3>.

This package provides new layer functions to tmap for drawing glyphs. A glyph is a small chart (e.g., donut chart) shown at specific map locations to visualize multivariate or time-series data. The functions work with the syntax of tmap and allow flexible control over size, layout, and appearance.

Efficient method for fitting nonparametric matrix trace regression model. The detailed description can be found in C. Lee, L. Li, H. Zhang, and M. Wang (2021). Nonparametric Trace Regression via Sign Series Representation. <arXiv:2105.01783>. The method employs the aggregation of structured sign series for trace regression (ASSIST) algorithm.

Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts.

Easily and flexibly insert Font Awesome icons into R Markdown documents and Shiny apps. These icons can be inserted into HTML content through inline SVG tags or i tags. There is also a utility function for exporting Font Awesome icons as PNG images for those situations where raster graphics are needed.

This package provides system native access to the font catalogue. As font handling varies between systems it is difficult to correctly locate installed fonts across different operating systems. The 'systemfonts' package provides bindings to the native libraries for finding font files that can then be used further by e.g. graphic devices.

This package contains an implementation of a high-quality splittable pseudorandom number generator. The generator is based on a cryptographic hash function built on top of the ThreeFish block cipher. See the paper "Splittable Pseudorandom Number Generators Using Cryptographic Hashing" by Claessen, Pałka for details and the rationale of the design.

It computes betas-select, coefficients after standardization in structural equation models and regression models, standardizing only selected variables. Supports models with moderation, with product terms formed after standardization. It also offers confidence intervals that account for standardization, including bootstrap confidence intervals as proposed by Cheung et al. (2022) <doi:10.1037/hea0001188>.

Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.

This package provides classes and methods for seismic data analysis. The base classes and methods are inspired by the python code found in the ObsPy python toolbox <https://github.com/obspy/obspy>. Additional classes and methods support data returned by web services provided by the IRIS DMC <http://service.iris.edu/>.

This package provides access to well-documented medical datasets for teaching. Featuring several from the Teaching of Statistics in the Health Sciences website <https://www.causeweb.org/tshs/category/dataset/>, a few reconstructed datasets of historical significance in medical research, some reformatted and extended from existing R packages, and some data donations.

This package provides a toolbox to handle and represent trophic networks in space or time across aggregation levels. This package contains a layout algorithm specifically designed for trophic networks, using dimension reduction on a diffusion graph kernel and trophic levels. Importantly, this package provides a layout method applicable for large trophic networks.

This package provides a collection of functions to search and download street view imagery ('Mapilary <https://www.mapillary.com/developer/api-documentation>) and to extract, quantify, and visualize visual features. Moreover, there are functions provided to generate Qualtrics survey in TXT format using the collection of street views for various research purposes.

This package provides a framework for the creation and use of Neural ordinary differential equations with the tensorflow and keras packages. The idea of Neural ordinary differential equations comes from Chen et al. (2018) <doi:10.48550/arXiv.1806.07366>, and presents a novel way of learning and solving differential systems.

Estimates joint marker (longitudinal) and survival (time-to-event) outcomes using variational approximations. The package supports multivariate markers allowing for correlated error terms and multiple types of survival outcomes which may be left-truncated, right-censored, and recurrent. Time-varying fixed and random covariate effects are supported along with non-proportional hazards.

The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset).

deMULTIplex is an R package for analyzing single-cell RNA sequencing data generated with the MULTI-seq sample multiplexing method. The package includes software to

1. Convert raw MULTI-seq sample barcode library FASTQs into a sample barcode UMI count matrix, and

2. Classify cell barcodes into sample barcode groups.

This package provides functions, documentation and example data to help divide geographic space into discrete polygons (zones). The functions are motivated by research into the merits of different zoning systems. A flexible ClockBoard zoning system is provided, which breaks-up space by concentric rings and radial lines emanating from a central point.

This package only contains and exports a single function realdot(x, y). It computes real(LinearAlgebra.dot(x, y)) while avoiding computing the imaginary part of LinearAlgebra.dot(x, y) if possible. The real dot product is useful when one treats complex numbers as embedded in a real vector space.

Ref::Util introduces several functions to help identify references in a smarter (and usually faster) way. The difference with conventional approach:

• No comparison against a string constant

• Supports blessed variables

• Supports tied variables and magic

• Ignores overloading

• Ignores subtle types

• Usually faster