This package lets you build complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as LeftOf, RightOf, TopOf, and Beneath and more. Other features include clustering, dendrograms and integration with ggplot2 generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.

This package contains data which are used by functions of the abc package which implements several Approximate Bayesian Computation (ABC) algorithms for performing parameter estimation, model selection, and goodness-of-fit.

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

This package allows the estimation of hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in the hierarchy, following the algorithm of Yang (Evolution, 1998, 52(4):950-956). Functions are also given to test via randomisations the significance of each F and variance components, using the likelihood-ratio statistics G.

A treemap is a space-filling visualization of hierarchical structures. This package offers great flexibility to draw treemaps.

This package provides tool for estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154, and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509.

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 is a package for parallel computing with a network of local and remote workers. It enables fast exchange of results between the workers through a Redis database. Key features include task queues, local caching, and sophisticated error handling.

This package helps you create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.

This package provides a set of predicates and assertions for checking the properties of UK-specific complex data types. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.

This package provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class family, link-glm, lm, glm and betareg. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the enriched_glm function that has the same interface as glm but results in objects of class enriched_glm. In addition to the usual components in a glm object, enriched_glm objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.

This package provides functions for importing, exporting, plotting and other manipulations of bitmapped images.

This package provides building blocks for the design and analysis of multiobjective optimization algorithms.

This package provides a dplyr back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features works with any database that has a DBI back end; more advanced features require SQL translation to be provided by the package author.

This package implements density, distribution functions, quantile functions and random generation functions for a large number of univariate and multivariate distributions.

This package performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. It includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.

This package provides a collection of some tests commonly used for identifying outliers.

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

This package provides miscellaneous functions for SciViews or general use, including tools to manage a temporary environment attached to the search path for temporary variables you do not want to save() or load(); test the current platform; showing progress bars, etc.

This package provides a graphical user interface for interactive Markov chain Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a posterior sample. The interface is powered by the Shiny web application framework and works with the output of MCMC programs written in any programming language (and has extended functionality for Stan models fit using the rstan and rstanarm packages).

This package provides a ggplot2 extension that enables the rendering of complex formatted plot labels (titles, subtitles, facet labels, axis labels, etc.). Text boxes with automatic word wrap are also supported.

This package provides methods for variable selection for AFT models.

This package provides an API for https://orcid.org. Functions include searching for people, searching by DOI, or searching by Orcid ID.