Streaming JSON (ndjson) has one JSON record per-line and many modern ndjson files contain large numbers of records. These constructs may not be columnar in nature, but it is often useful to read in these files and "flatten" the structure out to enable working with the data in an R data.frame-like context. Functions are provided that make it possible to read in plain ndjson files or compressed (gz) ndjson files and either validate the format of the records or create "flat" data.table structures from them.

This package provides a collection of tools to make working with physical measurements easier. One can convert between metric and imperial units, or calculate a dimension's unknown value from other dimensions' measurements.

The Rmisc library contains functions for data analysis and utility operations.

This package contains R functions and datasets detailed in the book "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.

This package computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g. soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method.

This package provides simple bindings to Unidata's udunits library.

This package provides functions that read and solve linear inverse problems (food web problems, linear programming problems).

The futile.options subsystem provides an easy user-defined options management system that is properly scoped. This means that options created via futile.options are fully self-contained and will not collide with options defined in other packages.

Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e., a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.

Partial application is the process of reducing the arity of a function by fixing one or more arguments, thus creating a new function lacking the fixed arguments. The curry package provides three different ways of performing partial function application by fixing arguments from either end of the argument list (currying and tail currying) or by fixing multiple named arguments (partial application). This package provides this functionality through the %<%, %-<%, and %><% operators which allows for a programming style comparable to modern functional languages. Compared to other implementations such a purrr::partial() the operators in curry composes functions with named arguments, aiding in autocomplete etc.

This package provides functions to handle basic input output. These functions always read and write UTF-8 (8-bit Unicode Transformation Format) files and provide more explicit control over line endings.

This package provides an interface to Amazon Web Services storage services, including Simple Storage Service (S3).

This package provides simple functions to compute and plot two types (sample-size- and coverage-based) rarefaction and extrapolation curves for species diversity (Hill numbers) based on individual-based abundance data or sampling-unit- based incidence data; see Chao and others (2014, Ecological Monographs) for pertinent theory and methodologies, and Hsieh, Ma and Chao (2016, Methods in Ecology and Evolution) for an introduction of the R package.

This package provides methods for cluster analysis. It is a much extended version of the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".

This is a package that allows conversion to and from data in JavaScript Object Notation (JSON) format. This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code and allows R programmers to read and convert JSON content to R objects. This is an alternative to the rjson package.

This package provides an interface from R to Python modules, classes, and functions. When calling into Python, R data types are automatically converted to their equivalent Python types. When values are returned from Python to R they are converted back to R types.

This package provides support for simple features, a standardized way to encode spatial vector data. It binds to GDAL for reading and writing data, to GEOS for geometrical operations, and to PROJ for projection conversions and datum transformations.

This package is designed to be used with Rscript to write shebang scripts that accept short and long options. Many users will prefer to use the packages optparse or argparse which add extra features like automatically generated help options and usage texts, support for default values, positional argument support, etc.

Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known.

This package implements time series clustering along with optimized techniques related to the dynamic time warping distance and its corresponding lower bounds. The implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included.

This package does local optimization using two derivatives and trust regions. Guaranteed to converge to local minimum of objective function.

This package lets you rarefy data, calculate diversity and plot the results.

This package allows users to create CSS grid and flexbox layouts for R/Shiny without needing to write custom CSS.

Feature Selection with Regularized Random Forest. This package is based on the randomForest package by Andy Liaw. The key difference is the RRF() function that builds a regularized random forest. Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener, Regularized random forest for classification by Houtao Deng, Regularized random forest for regression by Xin Guan. Reference: Houtao Deng (2013) <doi:10.48550/arXiv.1306.0237>.