ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target.

This package provides access to eoPred pretrained model hosted on ExperimentHub. Model was trained on placental DNA methylation preeclampsia samples using mixOmics splsda. There are two resources: 1. the model object, and 2. a testing data set used to demonstrate the function.

This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection.

The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA).

This package contains a SummarizedExperiment from the Yu et al. (2013) paper that performed the rat BodyMap across 11 organs and 4 developmental stages. Raw FASTQ files were downloaded and mapped using STAR. Data is available on ExperimentHub as a data package.

This package implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages.

The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.

The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.

The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.

The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.

The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.

The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.

The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.

The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.

The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.

The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.

This collection of utilities contains tooling and templates to assist in creating releases on GitHub and publishing them on PyPI. It is designed to be used by Robot Framework and tools and libraries in its ecosystem, but can naturally be used also by other projects.

This package provides a collection of programs for plotting SKEW-T,log p diagrams and wind profiles for data collected by radiosondes (the typical weather balloon-borne instrument). The format of this plot with companion lines to assess atmospheric stability are both standard in meteorology and difficult to create from basic graphics functions. Hence this package. One novel feature is being able add several profiles to the same plot for comparison. Use "help(ExampleSonde)" for an explanation of the variables needed and how they should be named in a data frame. See <https://github.com/dnychka/Radiosonde> for the package home page.

New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested and their performance accurately assessed. This requires densities that offer challenging properties to the novel sampling algorithms. One such popular problem is the Rosenbrock function. However, while its shape lends itself well to a benchmark problem, no codified multivariate expansion of the density exists. We have developed an extension to this class of distributions and supplied densities and direct sampler functions to assess the performance of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) <arXiv:1903.09556>.

An optimized method for identifying mutually exclusive genomic events. Its main contribution is a statistical analysis based on the Poisson-Binomial distribution that takes into account that some samples are more mutated than others. See [Canisius, Sander, John WM Martens, and Lodewyk FA Wessels. (2016) "A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>]. The mutations matrices are sparse matrices. The method developed takes advantage of the advantages of this type of matrix to save time and computing resources.

This package provides a modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See Gadaleta et al (2016) <doi:10.4018/IJQSPR.2016010102> for an overview of applicability domains.

Functionalities to automatically generate interactive visualizations for statistical results supported by ggfortify', such as time series, PCA, clustering and survival analysis, with plotly.js <https://plotly.com/> and ggplot2 style. The generated visualizations can also be easily extended using ggplot2 and plotly syntax while staying interactive.

Efficient object-oriented R6 dictionary capable of holding objects of any class, including R6. Typed and untyped dictionaries are provided as well as the usual dictionary methods that are available in other OOP languages, for example listing keys, items, values, and methods to get/set these.

Extracting desired data using the proper Census variable names can be time-consuming. This package takes the pain out of that process by providing functions to quickly locate variables and download labeled tables from the Census APIs (<https://www.census.gov/data/developers/data-sets.html>).