This package provides support for all calendars as specified in the Climate and Forecast (CF) Metadata Conventions for climate and forecasting data. The CF Metadata Conventions is widely used for distributing files with climate observations or projections, including the Coupled Model Intercomparison Project (CMIP) data used by climate change scientists and the Intergovernmental Panel on Climate Change (IPCC). This package specifically allows the user to work with any of the CF-compliant calendars (many of which are not compliant with POSIXt). The CF time coordinate is formally defined in the CF Metadata Conventions document.

This package estimates previously compiled regression models using the rstan package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

This package provides a comprehensive collection of color palettes, color maps, and tools to evaluate them.

R-coop offers implementations of covariance, correlation and cosine similarity. The implementations are fast and memory-efficient and their use is resolved automatically based on the input data, handled by R's S3 methods. Full descriptions of the algorithms and benchmarks are available in the package vignettes.

Dunn's test computes stochastic dominance & reports pairwise comparisons. This is done following a Kruskal-Wallis test (Kruskal and Wallis, 1952). It employs Dunn's z-test-statistic approximations for rank statistics, conducting k(k-1)/2 comparisons. The null hypothesis assumes that the probability of a randomly selected value from the first group being larger than one from the second group is one half, similar to the Wilcoxon-Mann-Whitney test. Dunn's test serves as a test for median difference and takes into account tied ranks.

Tools that can be used to reshape and restructure text data.

This package provides Wiener process distribution functions, namely the Wiener first passage time density, CDF, quantile and random functions. It additionally supplies a modelling function (wdm) and further methods for the resulting object.

Algorithms to find arrangements of non-overlapping circles.

This package provides functions used for local regression, likelihood and density estimation.

This package provides a fast implementation of hierarchical clustering.

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

This package implements the regularized Gaussian maximum likelihood estimation of the inverse of a covariance matrix. It uses Newton's method and coordinate descent to solve the regularized inverse covariance matrix estimation problem.

This package contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX code, and recoding variables.

This package provides pure R tools to read BMP format images. It is currently limited to 8 bit greyscale images and 24, 32 bit (A)RGB images.

This package provides an R interface to V8: Google's JavaScript and WebAssembly engine.

This package provides plotting functions for posterior analysis, model checking, and MCMC diagnostics. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling.

This package provides a collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future.

This package provides an interface to Amazon Web Services developer tools services, including version control, continuous integration and deployment, and more.

This package provides an implementation of the Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution".

This package contains some functions to help users (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. Functions in this package are supposed to be efficient and easy to use.

Aster models (Geyer, Wagenius, and Shaw, 2007, <doi:10.1093/biomet/asm030>; Shaw, Geyer, Wagenius, Hangelbroek, and Etterson, 2008, <doi:10.1086/588063>; Geyer, Ridley, Latta, Etterson, and Shaw, 2013, <doi:10.1214/13-AOAS653>) are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e.2g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, life table analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e.g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e.g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). There are also random effects versions of these models.

This package provides various R programming tools for plotting data, including:

• calculating and plotting locally smoothed summary function

• enhanced versions of standard plots

• manipulating colors

• calculating and plotting two-dimensional data summaries

• enhanced regression diagnostic plots

• formula-enabled interface to stats::lowess function

• displaying textual data in plots

• balloon plots

• plotting "Venn" diagrams

• displaying Open-Office style plots

• plotting multiple data on same region, with separate axes

• plotting means and confidence intervals

• spacing points in an x-y plot so they don't overlap

This package implements easy-to-use functions to generate 2-7 sets Venn plot in publication quality. ggVennDiagram plot Venn using well-defined geometry dataset and ggplot2. The shapes of 2-4 sets Venn use circles and ellipses, while the shapes of 4-7 sets Venn use irregular polygons (4 has both forms), which are developed and imported from another package venn. We provide internal functions to integrate shape data with user provided sets data, and calculated the geometry of every regions/intersections of them, then separately plot Venn in three components: set edges, set labels, and regions. From version 1.0, it is possible to customize these components as you demand in ordinary ggplot2 grammar.

This package provides ACE and AVAS methods for choosing regression transformations.