This package provides R functions implementing a standard unit testing framework, with additional code inspection and report generation tools.

Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.

This package performs complex string operations compactly and efficiently. It supports string interpolation jointly with over 50 string operations. It also enhances regular string functions (like grep() and co).

The vegan package provides tools for descriptive community ecology. It has most basic functions of diversity analysis, community ordination and dissimilarity analysis. Most of its multivariate tools can be used for other data types as well.

This package provides classes and methods for spatial objects that have a registered time column, in particular for irregular spatiotemporal data. The time column can be of any type, but needs to be ordinal. Regularly laid out spatiotemporal data (vector or raster data cubes) are handled by package stars'.

The Ziggurat generator for normally distributed random numbers, originally proposed by Marsaglia and Tsang (2000, https://doi.org/10.18637/jss.v005.i08) has been improved upon a few times starting with Leong et al (2005, https://doi.org/10.18637/jss.v012.i07). This package provides an aggregation for comparing different implementations in order to provide a 'faster but good enough' alternative for use with R and C++ code.

This package provides an easy way to fill an environment with active bindings that call a C++ function.

This package provides an implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the orf package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the ranger package (Wright & Ziegler, 2017) <arXiv:1508.04409>.

The ability to tune models is important. tune contains functions and classes to be used in conjunction with other tidymodels packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps.

Read in activity measurements from standard file formats used by circadian rhythm researchers, currently only ClockLab format, and process and plot the data. The central type of plot is the actogram, as first described in "Activity and distribution of certain wild mice in relation to biotic communities" by MS Johnson (1926) doi:10.2307/1373575.

This package provides a set of functions for data manipulation with list objects, including mapping, filtering, grouping, sorting, updating, searching, and other useful functions. Most functions are designed to be pipeline friendly so that data processing with lists can be chained.

This package provides features to build gradient color maps.

This package provides UI widget and layout functions for writing Shiny apps that work well on small screens.

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 provides a more comfortable interface to work with R data or source files in a key-value fashion.

This package provides tools to compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. Except for Steel's test and the JT test it also combines these tests across several blocks of samples.

This package provides functions to re-arrange, extract, and work with distances.

This package provides tools for importing and working with bibliographic references. It greatly enhances the bibentry class by providing a class BibEntry which stores BibTeX and BibLaTeX references, supports UTF-8 encoding, and can be easily searched by any field, by date ranges, and by various formats for name lists (author by last names, translator by full names, etc.). Entries can be updated, combined, sorted, printed in a number of styles, and exported. BibTeX and BibLaTeX .bib files can be read into R and converted to BibEntry objects.

This package provides a cross-platform Zip compression library for R. It is a replacement for the zip function, that does not require any additional external tools on any platform.

The choices of color palettes in R can be quite overwhelming with palettes spread over many packages with many different API's. This package aims to collect all color palettes across the R ecosystem under the same package with a streamlined API.

This package implements the RUV (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are RUV-2, RUV-4, RUV-inv, RUV-rinv, RUV-I, and RUV-III, along with various supporting algorithms.

This package provides classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).

Stringr is a consistent, simple and easy to use set of wrappers around the fantastic stringi package. All function and argument names (and positions) are consistent, all functions deal with "NA"'s and zero length vectors in the same way, and the output from one function is easy to feed into the input of another.

This package provides a wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.