This package provides functions that simplify submitting R scripts to a Slurm workload manager, in part by automating the division of embarrassingly parallel calculations across cluster nodes.

This package provides an efficient implementation of Kernel SHAP (Lundberg and Lee, 2017, <doi:10.48550/arXiv.1705.07874>) permutation SHAP, and additive SHAP for model interpretability. For Kernel SHAP and permutation SHAP, if the number of features is too large for exact calculations, the algorithms iterate until the SHAP values are sufficiently precise in terms of their standard errors. The package integrates smoothly with meta-learning packages such as tidymodels, caret or mlr3. It supports multi-output models, case weights, and parallel computations. Visualizations can be done using the R package shapviz.

This package provides an R interface to the vis.js JavaScript charting library. It allows an interactive visualization of networks.

This package provides support for the foreach looping construct. foreach is an idiom that allows for iterating over elements in a collection, without the use of an explicit loop counter. This package in particular is intended to be used for its return value, rather than for its side effects. In that sense, it is similar to the standard lapply function, but doesn't require the evaluation of a function. Using foreach without side effects also facilitates executing the loop in parallel.

This package provides streamlined data import and export infrastructure by making assumptions that the user is probably willing to make: import and export determine the data structure from the file extension, reasonable defaults are used for data import and export (e.g., stringsAsFactors=FALSE), web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly without explicit decompression, and fast import packages are used where appropriate. An additional convenience function, convert, provides a simple method for converting between file types.

Circular Statistics, from "Topics in Circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.

This is a lightweight package for computing different kinds of correlations. These correlations include partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight correlations, distance correlations and more.

Geometry shapes in R are typically represented by matrices (points, lines), with more complex shapes being lists of matrices (polygons). Geometries will convert various R objects into these shapes. Conversion functions are available at both the R level, and through Rcpp.

This package represents an implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization.

This package provides a means to mock a package function, i.e., temporarily substitute it for testing. It was designed as a drop-in replacement for the now deprecated testthat::with_mock() and testthat::local_mock().

Zoltar is a website that provides a repository of model forecast results in a standardized format and a central location. It supports storing, retrieving, comparing, and analyzing time series forecasts for prediction challenges of interest to the modeling community. This package provides functions for working with the Zoltar API, including connecting and authenticating, getting information about projects, models, and forecasts, deleting and uploading forecast data, and downloading scores.

This package implements numerically-stable Gauss-Hermite quadrature rules and utility functions for adaptive GH quadrature.

This package provides miscellaneous small tools and utilities. Many of them facilitate the work with matrices, e.g. inserting rows or columns, creating symmetric matrices, or checking for semidefiniteness. Other tools facilitate the work with regression models, e.g. extracting the standard errors, obtaining the number of (estimated) parameters, or calculating R-squared values.

OOMPA offers R packages for gene expression and proteomics analysis. OOMPA uses S4 classes to construct object-oriented tools with a consistent user interface. All higher level analysis tools in OOMPA are compatible with the eSet classes defined in BioConductor. The lower level processing tools offer an alternative to parts of BioConductor, but can also be used to enhance existing BioConductor packages.

This is a package to infer transmission trees from a dated phylogeny. It includes methods to simulate and analyze outbreaks. The methodology is described in Didelot et al. (2014) and Didelot et al. (2017).

This package provides the cumulative distribution function (CDF), quantile, and statistical power calculator for a collection of thresholding Fisher's p-value combination methods, including Fisher's p-value combination method, truncated product method and, in particular, soft-thresholding Fisher's p-value combination method which is proven to be optimal in some context of signal detection. The p-value calculator for the omnibus version of these tests are also included.

This package provides themes for use with Shiny. It includes several Bootstrap themes, which are packaged for use with Shiny applications.

This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.

This package implements core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with DE matrices and count matrices, a collection of functions for manipulating and plotting data via ggplot2, and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP, collapsing vertices of each cluster in the graph, and propagating graph labels.

This package provides functions to fit kernel density functions to data on temporal activity patterns of animals; estimate coefficients of overlapping of densities for two species; and calculate bootstrap estimates of confidence intervals.

This package represents a collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.

Cyclomatic complexity is a software metric, used to indicate the complexity of a program. It is a quantitative measure of the number of linearly independent paths through a program's source code. This package provides tools to compute this metric.

This package provides a reticulate wrapper for the Python package anndata. It provides a scalable way of keeping track of data and learned annotations. It is used to read from and write to the h5ad file format.

This package lets you fit generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) <doi:10.1080/10618600.1995.10474663>.