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.

This package provides procedures for model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects. Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), are supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models.

This package provides a placeholder for the Liberation fontset intended for the fontquiver package. This fontset covers the 12 combinations of families (sans, serif, mono) and faces (plain, bold, italic, bold italic) supported in R graphics devices.

This package implements tools designed to collect and organize Twitter data via Twitter's REST and stream Application Program Interfaces (API).

This package provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure.

This package provides a cross-platform solution to open files, directories or URLs with their associated programs.

This package provides a minimal set of predicates and assertions used by the assertive package. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package provides an implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature.

This package provides an R implementation of an extension of the BayeScan software for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection.

This package provides functions to impute using random forest. It operates under full conditional specifications (multivariate imputation by chained equations).

This package provides suite of functions to work with regression model broom::tidy() tibbles. The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more.

This package provides an iteration of the DEoptim function. It performs global optimization by differential evolution.

This package provides functions for viewing 2D and 3D data, including perspective plots, slice plots, surface plots, scatter plots, etc. It includes data sets from oceanography.

This package provides tools to execute arbitrary R or C functions some time after the current time, after the R execution stack has emptied.

Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of hardhat is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input.

ICGE is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.

This package provides tools to generate a violin point plot, a combination of a violin/histogram plot and a scatter plot by offsetting points within a category based on their density using quasirandom noise.

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 a fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.

This package provides S3 classes and methods to create and work with year-quarter, year-month and year-isoweek vectors. Basic arithmetic operations (such as adding and subtracting) are supported, as well as formatting and converting to and from standard R date types.

This package lets you access services specified in OpenAPI (formerly Swagger) format. It is not a code generator. The client is generated dynamically as a list of R functions.

This package provides a suite of methods for powerful and robust microbiome data analysis, including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances, Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature- based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA:

• PERMANOVA using the Freedman-Lane permutation scheme,

• PERMANOVA omnibus test using multiple matrices, and

• analytical approach to approximating PERMANOVA p-value.

Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.

Estimate a suite of normalizing transformations, including a new adaptation of a technique based on ranks which can guarantee normally distributed transformed data if there are no ties: ordered quantile normalization (ORQ). ORQ normalization combines a rank-mapping approach with a shifted logit approximation that allows the transformation to work on data outside the original domain. It is also able to handle new data within the original domain via linear interpolation. The package is built to estimate the best normalizing transformation for a vector consistently and accurately. It implements the Box-Cox transformation, the Yeo-Johnson transformation, three types of Lambert WxF transformations, and the ordered quantile normalization transformation. It estimates the normalization efficacy of other commonly used transformations, and it allows users to specify custom transformations or normalization statistics. Finally, functionality can be integrated into a machine learning workflow via recipes.

The GNU Scientific Library (or GSL) is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines. There are over 1000 functions in total with an extensive test suite. The RcppGSL package provides an easy-to-use interface between GSL data structures and R using concepts from Rcpp which is itself a package that eases the interfaces between R and C++.