Radiance is a web application environment, which is sort of like a web framework, but more general, more flexible. It should let you write personal websites and generally deployable applications easily and in such a way that they can be used on practically any setup without having to undergo special adaptations.

This package contains the data employed in the vignette of the PathNet package. These data belong to the following publication: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J., Source Code for Biology and Medicine 2012 Sep 24;7(1):10.

Optimist is a commandline option parser for Ruby that just gets out of your way. One line of code per option is all you need to write. For that, you get a nice automatically-generated help page, robust option parsing, command subcompletion, and sensible defaults for everything you don't specify.

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.

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.

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

This package contains functions to implement the methodology and considerations laid out by Marks et al. in the article "Measuring abnormality in high dimensional spaces: applications in biomechanical gait analysis". Using high-dimensional datasets to measure a subject's overall level of abnormality as compared to a reference population is often needed in outcomes research.

This package is a feature selection package of the mlr3 ecosystem. It selects the optimal feature set for any mlr3 learner. The package works with several optimization algorithms e.g. random search, Recursive feature elimination, and genetic search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.

scoreInvHap can get the samples inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for 21 inversions.

signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures coming from public literature, based on gene expression values, and return single-sample (-cell/-spot) scores. Currently, signifinder collects more than 70 distinct signatures, relating to multiple tumors and multiple cancer processes.

This package provides tools for analyzing R expressions or blocks of code and determining the dependencies between them. It focuses on R scripts, but can be used on the bodies of functions. There are many facilities including the ability to summarize or get a high-level view of code, determining dependencies between variables, code improvement suggestions.

This package creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().

The IPC::Run3 module allows you to run a subprocess and redirect stdin, stdout, and/or stderr to files and perl data structures. It aims to satisfy 99% of the need for using system, qx, and open3 with a simple, extremely Perlish API and none of the bloat and rarely used features of IPC::Run.

An R package providing extended biological annotations for the SomaScan Assay, a proteomics platform developed by SomaLogic Operating Co., Inc. The annotations in this package were assembled using data from public repositories. For more information about the SomaScan assay and its data, please reference the SomaLogic/SomaLogic-Data GitHub repository.

In many analyses, a large amount of variables have to be tested independently against the trait/endpoint of interest, and also adjusted for covariates and confounding factors at the same time. The major bottleneck in these is the amount of time that it takes to complete these analyses. With RegParallel, a large number of tests can be performed simultaneously. On a 12-core system, 144 variables can be tested simultaneously, with 1000s of variables processed in a matter of seconds via nested parallel processing. Works for logistic regression, linear regression, conditional logistic regression, Cox proportional hazards and survival models, and Bayesian logistic regression. Also caters for generalised linear models that utilise survey weights created by the survey CRAN package and that utilise survey::svyglm'.

This package performs angle-based outlier detection on a given data frame. It offers three methods to process data:

1. full but slow implementation using all the data that has cubic complexity;

2. a fully randomized method;

3. a method using k-nearest neighbours.

These algorithms are well suited for high dimensional data outlier detection.

Suppose we have data that has so many series that it is hard to identify them by their colors as the differences are so subtle. With gghighlight we can highlight those lines that match certain criteria. The result is a usual ggplot object, so it is fully customizable and can be used with custom themes and facets.

This package is for designing Crispr/Cas9 and Prime Editing experiments. It contains functions to (1) define and transform genomic targets, (2) find spacers (4) count offtarget (mis)matches, and (5) compute Doench2016/2014 targeting efficiency. Care has been taken for multicrispr to scale well towards large target sets, enabling the design of large Crispr/Cas9 libraries.

Method for identification of spatial domains and spatially-aware clustering in spatial transcriptomics data. The method generates spatial domains with smooth boundaries by smoothing gene expression profiles across neighboring spatial locations, followed by unsupervised clustering. Spatial domains consisting of consistent mixtures of cell types may then be further investigated by applying cell type compositional analyses or differential analyses.